RU2649078C2 - System and methods to compensate doppler effects in wireless systems with distributed input - distributed output - Google Patents

Abstract

FIELD: wireless communication equipment.

SUBSTANCE: invention relates to multi-user wireless data communication systems. Multi-user multi-antenna system contains: a centralized unit connected to communicate with a plurality of distributed transceiver stations or antennas; a network consisting of wired or wireless communication lines and used as a communication channel of the transport network. In this case, the centralized unit is configured able to communicate with the plurality of distributed transceiver stations or antennas for adaptive reconfiguration of simultaneous communication within one frequency band between the distributed transceiver stations or antennas and multiple users in order to reduce the influence of Doppler effects caused by the user movements or by changes in the data distribution environment.

EFFECT: technical result is the ability to compensate for the negative influence of the Doppler effect.

35 cl, 62 dwg

Description

This application is a partial continuation of the following pending US patent applications.

US Patent Application No. 12 / 917,257, filed November 1, 2010, “Systems and methods for coordinating transmissions in distributed wireless systems through user clustering”; US Patent Application No. 12 / 802,988, filed June 16, 2010, “Interference Management, Handoff Strategy, Power Management, and Adaptation of Communication Lines in Communication Systems with Distributed Input - Distributed Output (DIDO)”; US Patent Application No. 12 / 802,976, filed June 16, 2010, “System and Method for Optimizing Noise Reduction in DIDO Based on Measurement of Signal Strength,” US Pat. No. 8,170,081, received May 1, 2012; US Patent Application No. 12 / 802,974, filed June 16, 2010, “System and Method for Managing Cluster Relay Transmissions of Clients Passing Through Multiple DIDO Clusters”; US Patent Application No. 12 / 802,989, filed June 16, 2010, “A System and Method for Managing Client Relay Transmissions between Different Distributed Input-Distributed Output (DIDO) Networks Based on Measured Client Speed”; US Patent Application No. 12 / 802,958, filed June 16, 2010, “System and Method for Power Control and Antenna Grouping in Distributed Input-Distributed Output (DIDO) Networks”; US Patent Application No. 12 / 802,975, filed June 16, 2010, “System and Method for Adapting Communication Lines in Multi-Carrier DIDO Systems”; US patent application No. 12 / 802,938, filed June 16, 2010, "System and method for interpolating precoding in DIDO systems with multiple carriers"; US Patent Application No. 12 / 630,627, filed December 3, 2009, “System and Method for Wireless Communication with Distributed Antennas”; application for US patent No. 12/143,503, filed June 20, 2008, "System and method for wireless communication with distributed input - distributed output", US patent 8,160,121 which was received on April 17, 2009; Application for US patent No. 11/894,394, filed August 20, 2007, "System and method for wireless communication with distributed input - distributed output", US patent 7,599,420 which was received on October 6, 2009; application for US patent No. 11/894,362, filed August 20, 2007, "System and method for wireless communication with distributed input - distributed output", US patent 7,633,994 which was received December 15, 2009; application for US patent No. 11/894,540, filed August 20, 2007, "System and method for wireless communication with distributed input - distributed output", US patent 7,636,381 for which was received December 22, 2009; Application for US patent No. 11/256,478, filed October 21, 2005, "System and method for communication with spatially multiplexed tropospheric scattering", US patent 7,711,030 which was received on May 4, 2010; application for US patent No. 10/817,731, filed April 2, 2004, "System and method for improving the quality of communication by means of waves with almost vertical ionospheric reflection (NVIS) using space-time coding", US patent 7,885,354 which was received on February 28, 2011 g.

State of the art

Prior art multi-user wireless systems may include only one base station or several base stations.

A single WiFi base station (e.g., using 2.4 GHz 802.11b, g, or n protocols) connected to a broadband wired Internet connection in an area where there are no other WiFi access points (e.g., a WiFi access point connected to a DSL inside a rural home) is an example of a relatively simple multi-user wireless system, which is a single base station shared by one or more users within its transmission range. If the user is in the same room as the wireless access point, then, as a rule, the user will have a high-speed communication line with some transmission errors (for example, packet loss may occur due to interference sources at a frequency of 2.4 GHz, such as microwave ovens, but not due to the use of the spectrum with other WiFi devices). If the user is at an average distance or there are several obstacles between the user and the WiFi access point, then the user is likely to have a communication line at an average speed. If the user approaches the edge of the WiFi access point's transmission range, then he will probably notice a low speed of the communication line and may experience intermittent loss of communication if changes in the channel affect the signal-to-noise ratio (SNR) of the signal and it falls below acceptable levels . And finally, if the user is outside the transmission range of the WiFi base station, he will have no connection at all.

When many users access the WiFi base station at the same time, the available bandwidth of the data channel is shared between them. At a particular point in time, different users will usually have different bandwidth requirements for the WiFi base station, but from time to time when the aggregate bandwidth requirements exceed the bandwidth available to users of the WiFi base station, some or all users will receive less bandwidth data channel than required by them. In a limiting situation where a large number of users share a WiFi access point, the throughput for each user can slow down to “crawl” and, even worse, the throughput for each user can reach short-term intervals, separated by long periods of complete lack of bandwidth data channel while other users are being served. Such “volatile” data delivery can degrade the performance of some applications, for example, transmitting a media stream.

Adding additional WiFi base stations in situations with a large number of users can only help to some extent. Within the 2.4 GHz ISM band, in the USA there are 3 non-interfering channels that can be used for WiFi, and if each of the 3 WiFi base stations in the same coverage area is capable of using different non-interfering channels, then the total bandwidth of the coverage area for many users will be increased by 3 times. But, besides this, adding more WiFi base stations in one coverage area will not increase the aggregate bandwidth, as they will begin to share the same available spectrum among themselves, effectively using time division multiplexed access (TDMA), using the spectrum in turn . A similar situation is often observed in coverage areas with a high population density, such as apartment buildings. For example, in a large apartment building, a user with a WiFi adapter can often have very poor bandwidth due to dozens of other interfering WiFi networks (for example, in other apartments) serving other users in the same coverage area, even if the access point the user is located in the same room as the client device that accesses the base station. Although the quality of the communication line is likely to be good in this situation, the user may receive interference from neighboring WiFi adapters operating in the same frequency range, which will reduce the effective bandwidth for the user.

Existing multi-user wireless systems, including both unlicensed spectrum (e.g. WiFi) and licensed spectrum, have several limitations. Limitations include coverage, downlink (DL) data rate and uplink (UL) data rate. The main goals of next-generation wireless systems, such as WiMAX and LTE, are to improve the coverage and speed of data transmission over DL and UL through technology with multiple inputs - multiple outputs (MIMO). MIMO is configured to use multiple antennas on the transmission sides and receive wireless communication lines to improve the quality of the communication line (resulting in wider coverage) or data transfer speed (by creating multiple spatial channels that do not interfere with each other). However, even if a sufficient data rate is available to each user (it should be noted that the terms “user” and “client” are used interchangeably in the present description), it is recommended to use channel diversity to obtain channels that do not interfere with each other for many users (and not for single user) in accordance with the multi-user MIMO technology (MU-MIMO). See, for example, the following publications:

G. Caire and S. Shamai, “On the achievable throughput of a multiantenna Gaussian broadcast channel”, IEEE Trans. Info Th., Volume 49, pp. 1691-1706, July 2003.

P. Viswanath and D. Tse, “Sum capacity of the vector Gaussian broadcast channel and uplink-downlink duality”, IEEE Trans. Info Th., Volume 49, pp. 1912-1921, August 2003

S. Vishwanath, N. Jindal, and A. Goldsmith, “Duality, achievable rates, and sum-rate capacity of Gaussian MIMO broadcast channels”, IEEE Trans. Info Th., Volume 49, pp. 2658-2668, October 2003.

W. Yu and J. Cioffi, “Sum capacity of Gaussian vector broadcast channels”, IEEE Trans. Info Th., Volume 50, pp. 1875-1892, September 2004.

M. Costa, “Writing on dirty paper,” IEEE Transactions on Information Theory, Volume 29, pp. 439-441, May 1983.

M. Bengtsson, “A pragmatic approach to multi-user spatial multiplexing,” Proc. of Sensor Array and Multichannel Sign. Proc. Workshop, pp. 130-134, August 2002.

K.-K. Wong, R.D. Murch, and K.B. Letaief, “Performance enhancement of multiuser MIMO wireless communication systems”, IEEE Trans. Comm., Volume 50, pp. 1960-1970, December 2002.

M. Sharif and B. Hassibi, “On the capacity of MIMO broadcast channel with partial side information”, IEEE Trans. Info Th., Volume 51, pp. 506-522, February 2005.

For example, in 4 × 4 MIMO systems (i.e., with four transmitting and four receiving antennas) with a 10 MHz bandwidth, 16-QAM modulation, and forward error correction coding (FEC) with a 3/4 coefficient (reduced spectral efficiency - 3 bit / s / Hz) the ideal peak data rate achievable at the physical level for each user is 4 × 30 Mbit / s = 120 Mbit / s, which far exceeds the necessary speed for delivering high-definition video content (which may only require ~ 10 Mbps). In MU-MIMO systems with four transmitting antennas, four users and one antenna for each user in ideal scenarios (i.e. with independent identically distributed (iid) channels), these four users can share the downlink data rate, and the spatial channel diversity can be used to create four parallel communication lines for transmitting data to users at a speed of 30 Mbps.

Various MU-MIMO schemes were proposed as part of the LTE standard, as described, for example, in 3GPP, “Multiple Input Multiple Output in UTRA”, 3GPP TR 25.876, version 7.0.0, March 2007; 3GPP, “Base Physical channels and modulation”, TS 36.211, version 8.7.0, May 2009; and 3GPP, “Multiplexing and channel coding”, TS 36.212, version 8.7.0, May 2009. However, these schemes can only provide a twofold increase in DL data rate with four transmit antennas. Practical implementation of MU-MIMO technologies in standard and private cellular communication systems performed by companies such as ArrayComm (see, for example, ArrayComm, “Field-proven results”, http://www.arraycomm.com/serve.php?page = proof), led to an approximately threefold increase (with four transmit antennas) in DL data transfer rates through spatial division multiple access (SDMA). A key limitation of MU-MIMO schemes in cellular networks is the lack of spatial diversity on the transmission side. Spatial diversity is a function of antenna diversity and multipath angular propagation in wireless links. In cellular communication systems containing MU-MIMO technology, transmitting antennas at a base station are typically grouped together and positioned at a distance of one or two wavelengths due to the limited usable area on the antenna support structures (referred to herein as “towers” regardless of whether they are tall or not) and due to restrictions imposed on the location of these towers. In addition, the multipath angular distribution is low because cellular towers are typically located high (10 meters or higher) above obstacles to provide wider coverage.

Other practical problems associated with the deployment of a cellular communication system include the excessive cost and limited availability of locations for cellular antennas (for example, due to municipal restrictions on the placement of antennas, the cost of real estate, physical obstacles, etc.), and also the cost and / or availability of network connections to transmitters (referred to herein as a “transport communications network"). Additionally, in cellular communication systems, access to customers located deep inside buildings is often difficult due to losses in walls, ceilings, floors, furniture, and other obstacles.

Of course, the very concept of a cellular structure for wireless communication in a global network involves a rather rigid placement of cellular towers, frequency alternation between adjacent cells and often sectorization to avoid interference between transmitters (base stations or users) using the same frequency. As a result, a particular sector of a particular cell is a DL and UL spectrum block shared by all users in a cell sector, which then these users share mainly only in the time domain. For example, cellular communication systems based on time division multiple access (TDMA) and code division multiple access (CDMA) divide the spectrum among users in the time domain. By adding sectorization to such cellular systems, one can achieve two to three times the gain in the spatial domain. Additionally, combining such a cellular communication system with a MU-MIMO system, such as described previously, it is possible to achieve an additional two to three times the gain in the space-time domain. But considering that the cells and sectors of a cellular communication system are usually located in fixed places (which is often dictated by the possible locations of the towers), even such limited advantages are difficult to use if user density (or data transfer rate requirements) at a specific time is not enough corresponds to the location of the tower / sector. The consequences of this effect are often experienced today by smartphone users, for example, a user can talk on the phone or download a web page without any delay, and then after moving (or even moving) to a new place, he suddenly sees a sharp decrease in speech quality, or an extreme slowdown in web loading -pages, or even complete loss of connection. But the next day, the user can observe completely opposite phenomena in the same places. The likely observations of the user (assuming that the environmental conditions have not changed) are a consequence of the fact that the density of users (or the requirements for data transfer speed) varies greatly, and the available total spectrum (and thus the overall data transfer rate using technologies prior art) shared by users at a given location is, for the most part, constant.

Additionally, prior art cellular communication systems rely on the use of different frequencies in different adjacent cells, typically three different frequencies. For a specific spectrum value, this reduces the available data rate by three times.

Thus, prior art cellular communication systems can lose up to three times the spectrum utilization due to the cellular structure and can improve up to three times the spectrum utilization due to sectorization and up to three times due to MU-MIMO technologies, which ultimately leads to 3 * 3/3 = threefold increase in the potential use of the spectrum. Further, such a frequency range is usually divided between users in the time domain, depending on which sector of which cell users enter at a particular moment. There is still additional inefficiency, resulting in the fact that the requirements for the data rate of a particular user are usually independent of their location, but the available data rate depends on the quality of the communication line between the user and the base station. For example, a user remote from the cellular base station will typically have a lower available data rate than a user closer to the base station. Since the data transfer rate is usually shared by all users in a particular cellular sector, as a result, all users are affected by high data transfer requirements from remote users with poor quality of the communication line (for example, at the edge of the cell), since such users continue to require the same data rate and consume more shared spectrum to achieve this speed.

Other proposed spectrum separation systems, such as those used by WiFi (e.g., 802.11b, g, and n) and proposed by White Spaces Coalition, divide the spectrum very inefficiently, since simultaneous transmissions by base stations within the user's range result in interference, resulting in systems apply collision avoidance and sharing protocols. Such spectrum sharing protocols are in the time domain, and thus, when there are a large number of interfering base stations and users, no matter how efficiently each of the base stations uses the spectrum itself, collectively the base stations are limited to sharing the time domain for spectra used by each other. Other prior art spectrum sharing systems likewise rely on similar techniques for suppressing interference among base stations (both cellular base stations with antennas on towers and small-scale base stations such as WiFi access points (APs)). These methods include limiting the transmit power from the base station to limit the interference range; beam formation (by means of synthetic or physical means) to narrow the area of interference; time domain spectrum multiplexing and / or MU-MIMO technology with multiple clustered antennas on a user device, base station, or both. In the case of advanced cellular networks already in place or planned today, often many of these methods are applied simultaneously.

But since even advanced cellular communication systems can achieve only about a three-fold increase in spectrum usage compared to a single spectrum user, it is obvious that all these technologies almost do not increase the aggregate data transfer rate among shared users for a specific coverage area. In particular, since the scaling of a particular coverage area depends on the number of users, it becomes increasingly difficult to scale the available data rate within a given spectrum value in order to keep up with the growing number of users. For example, in cellular communication systems, in order to increase the aggregate data rate within a specific area of a cell, they are usually divided into smaller cells (often called nanocells or femtocells). Such small cells can become extremely expensive, given the restrictions on the location of the towers and the requirement for towers to be arranged in a properly structured way to provide coverage with a minimum of “dead zones” and to avoid interference between adjacent cells using the same frequencies. Essentially, you need to plan the coverage area, identify the available locations for towers or base stations, and then, taking into account these limitations, the developers of a cellular communication system must achieve the best possible option. Of course, if user requirements for data transfer speeds increase over time, then cellular system developers must again and again re-plan the coverage area, try to find locations for towers or base stations, and again take into account the restrictions created by the circumstances. Moreover, very often there is no good solution, which leads to the appearance of dead zones or inappropriate aggregate performance in terms of data transfer rate in the coverage area. In other words, stringent requirements for the physical placement of a cellular communication system, designed to help avoid interference between towers or base stations using the same frequency, result in significant difficulties and limitations in designing a cellular communication system, often unable to meet user speed requirements data transfer and coverage.

The so-called “joint” and “cognitive” radio communication systems of the prior art are aimed at increasing the use of the spectrum in a given area by using intelligent algorithms within the radio communication systems to minimize mutual interference and / or the ability to “listen” to the use of the spectrum by others, while waiting for the channel won't get clean. Such systems are proposed to be used, in particular, in the unlicensed spectrum to increase the use of such a spectrum.

Mobile Decentralized Network (MANET) (see http://en.wikipedia.org/wiki/Mobile_ad_hoc_network) is an example of a joint self-organizing network designed to provide peer-to-peer communication, and can be used to establish communication among radio systems without cellular infrastructure, and when sufficiently low communication power can potentially reduce interference for simultaneous transmissions that are out of range of communication distance from each other. A huge number of routing protocols were proposed and implemented for the MANET system (see http://en.wikipedia.org/wiki/List_of_ad-hoc_routing_protocols for a list of dozens of routing protocols in a wide range of classes), but they are united by the fact that they all represent These are methods of routing (for example, relaying) transmissions aimed at minimizing transmitter interference within the available spectrum with the aim of certain paradigms of efficiency or reliability.

All prior art multi-user wireless systems aim to improve spectrum utilization in a particular coverage area using technologies that allow the simultaneous use of spectrum by base stations and multiple users. It is noteworthy that in all these cases, the technologies used for the simultaneous use of the spectrum by base stations and multiple users achieve simultaneous use of the spectrum by multiple users by suppressing interference for signals from multiple users. For example, in the case of 3 base stations, each of which uses different frequencies to transmit to one of 3 users, the interference is suppressed because these 3 transmissions go through 3 different frequencies. In the case of sectorization from the base station to 3 different users, for every 180-degree deviation relative to the base station, interference is suppressed because the beam formation prevents these 3 transmissions from overlapping by any user.

When such technologies are supported by the use of MU-MIMO and, for example, each base station has 4 antennas, this gives the potential opportunity to increase the throughput of the downlink by 4 times due to the creation of four non-interfering spatial channels in the direction of users in a specific coverage area . But this is still the case when, to suppress interference during multiple simultaneous transmissions to multiple users in different coverage areas, some technology must be applied.

And, as described previously, such prior art technologies (eg, cellular network creation, sectorization), as a rule, not only increase the cost of a multi-user wireless system and / or reduce deployment flexibility, but also, as a rule, meet physical or practical aggregate bandwidth limits in a particular coverage area. For example, in a cellular communication system, there may not be enough available space to install more base stations to create smaller cells. And in the MU-MIMO system, given the clustered antenna diversity at each location of the base station, limited spatial diversity results in an asymptotically decreasing gain in throughput as more antennas are added to the base station.

Additionally, in the case of multi-user wireless systems in which the location and density of users are unpredictable, this leads to unpredictable (often with drastic changes) bandwidth, which is inconvenient for the user and makes some applications (for example, the provision of services requiring predictable bandwidth) impractical or reduces their quality. Thus, multi-user wireless systems of the prior art leave much to be desired in terms of the ability to provide users with predictable and / or high-quality services.

Despite the outstanding development and complexity that have long been associated with the development of multi-user wireless systems of the prior art, there are common topics: transmissions are distributed between different base stations (or special transceivers) and structured and / or controlled so as to avoid a specific user on the receiver interacting with each other of radio frequency signal transmissions from various base stations and / or various special transceivers.

In other words, it is considered unavoidable that if a user receives transmissions from more than one base station or a special transceiver at the same time, interference due to multiple simultaneous transmissions will reduce the SNR and / or signal bandwidth for the user, which if such a decrease is serious enough , will lead to the loss of all or some potential data (or analog information), which otherwise the user should have accepted.

Thus, in a multi-user wireless system, it is necessary to apply one or more approaches to spectrum sharing in order to avoid or to suppress such interference for users created by multiple base stations or special transceivers operating on the same frequency simultaneously. In the prior art, a huge number of approaches have been developed to avoid such interference, including controlling the physical location of base stations (e.g., creating cellular networks), limiting the output power of base stations and / or special transceivers (e.g., limiting transmission distance), beamforming / sectorization and time domain multiplexing. In other words, all spectrum sharing systems are aimed at circumventing the limitations of multi-user wireless systems, consisting in that when many base stations and / or special transceivers transmitting simultaneously on the same frequency, the same user receives as a result of interference Decreases or destroys data throughput for this user. If a large percentage or all users in a multi-user wireless system are susceptible to interference from multiple base stations and / or special transceivers (for example, in the event of a malfunction of a multi-user wireless system component), this can lead to a situation where the combined throughput of the multi-user wireless system is significant will decrease or the system will become inoperative.

Multiuser wireless systems of the prior art complicate the problem and impose restrictions on wireless networks, which often leads to a situation where the quality of services for a particular user (for example, in terms of available bandwidth, delay, predictability, reliability) is affected by the use of spectrum by other users in the zone coverings. Given the growing demand for aggregate bandwidth within a wireless spectrum shared by many users and the increasing number of applications based on the reliability, predictability and low latency of a multi-tenant wireless network for a particular user, it is obvious that prior art multi-tenant wireless communication technology has a plurality of restrictions. Indeed, given the limited availability of a spectrum suitable for certain types of wireless communications (for example, wavelengths that effectively penetrate walls of a building), it may be that prior art wireless technologies will not be sufficient to meet the growing demand for bandwidth, which must be reliable, predictable and have a low latency.

The prior art related to the present invention describes beamforming systems and methods for controlling the zero position of an antenna pattern in multi-user scenarios. Beam shaping was originally conceived to maximize the signal-to-noise ratio (SNR) when received by dynamically adjusting the phase and / or amplitude of the signals (ie, weight when shaping the beam) supplied to the antenna arrays, thus focusing the energy in the direction user. In a multi-user scenario, beamforming can be used to suppress sources of interference and maximize the signal-to-noise-to-noise ratio (SINR). For example, when beamforming is used in a wireless link receiver, weights are calculated to create zeros in the direction of the interference sources. When beamforming is used in a transmitter in multi-user downlink scenarios, weights are calculated so as to pre-suppress inter-user interference and maximize SINR for each user. Alternative embodiments for multi-user systems, such as BD precoding, calculate precoding weights to maximize throughput in the downstream broadcast channel. At the same time, pending applications incorporated herein by reference describe the technologies mentioned above (see pending applications for specific references at the same time).

Brief Description of the Drawings

For a better understanding of the present invention, the following detailed description should be considered in conjunction with the drawings described below.

In FIG. 1 shows a primary DIDO cluster surrounded by neighboring DIDO clusters in one embodiment of the present invention.

In FIG. 2 illustrates frequency division multiple access (FDMA) technologies used in one embodiment of the present invention.

In FIG. 3 illustrates time division multiple access (TDMA) technologies used in one embodiment of the present invention.

In FIG. 4 illustrates various types of interfering zones, as discussed in one embodiment of the present invention.

In FIG. 5 shows the structure used in one embodiment of the present invention.

In FIG. Figure 6 shows the SER plot versus SNR, taking SIR = 10 dB, for the target client in the interfering area.

In FIG. 7 shows a SER plot obtained by two IDCI precoding methods.

In FIG. 8 illustrates an example scenario in which a target client moves from a primary DIDO cluster to an interfering cluster.

In FIG. 9 shows the signal-to-noise interference-to-noise ratio (SINR) versus distance (D).

In FIG. Figure 10 shows the characteristic of the frequency of occurrence of erroneous symbols (SER) for three scenarios with 4-QAM modulation in narrow-band channels with shallow fading.

In FIG. 11 shows an IDCI precoding method in accordance with one embodiment of the present invention.

In FIG. 12 illustrates a change in SINR in one embodiment depending on the distance of the client’s location from the center of the main DIDO clusters.

In FIG. 13 shows an embodiment in which a SER value is obtained for 4-QAM modulation.

In FIG. 14 shows an embodiment of the present invention in which a state machine implements a handoff algorithm.

In FIG. 15 illustrates one embodiment of a handoff strategy in the presence of shading.

In FIG. 16 shows the hysteresis loop mechanism when switching between any two states shown in FIG. 93.

In FIG. 17 illustrates one embodiment of a DIDO power control system.

In FIG. Figure 18 shows a plot of SER versus SNR assuming four DIDO transmit antennas and four clients in different scenarios.

In FIG. 19 shows the power density of traffic rules as a function of the distance from the RF source for various transmit power values in accordance with one embodiment of the present invention.

In FIG. 20a-b show various distributions of low power antennas and high power antennas for distributed DIDO antennas.

In FIG. 21a-b show two power distributions corresponding to the configurations shown in FIG. 20a and 20b, respectively.

In FIG. 22a-b show the velocity distribution for the two scenarios shown in FIG. 99a and 99b, respectively.

In FIG. 23 illustrates one embodiment of a power management DIDO system.

In FIG. 24 illustrates one embodiment of a method that iterates over all antenna groups according to a round-robin scheduling policy for transmitting data.

In FIG. 25 shows a comparison of the performance of an uncoded SER value in power control with antenna grouping and the standard eigenmode selection in US 7,636,381.

In FIG. 26a-c illustrate three scenarios in which the BD precoding dynamically adjusts the precoding weights to account for different power levels in the wireless links between the DIDO antennas and the clients.

In FIG. 27 shows the amplitude in channels with low frequency selectivity (assuming β = 1) in the delay region or the current PDP (upper graph) and the frequency domain (lower graph) for 2 × 2 DIDO systems.

In FIG. 28 shows one embodiment of a channel matrix frequency response for DIDO 2 × 2 with one antenna per client.

In FIG. Figure 29 shows an embodiment of the frequency response of a channel matrix for DIDO 2 × 2 with one antenna per client for channels characterized by high frequency selectivity (for example, with β = 0.1).

In FIG. 30 shows an example SER for various QAM schemes (i.e. 4-QAM, 16-QAM, 64-QAM).

In FIG. 31 illustrates one embodiment of a method that implements Link Adaptation (LA) technologies.

In FIG. 32 shows the SER characteristic for one embodiment of a Link Adaptation (LA) technology.

In FIG. 33 shows the matrix elements in equation (28) depending on the OFDM tone index for a 2 × 2 DIDO system with N _FFT = 64 and L ₀ = 8.

In FIG. 34 shows the dependence of SER on SNR for L ₀ = 8, M = N _t = 2 transmitting antennas and a variable number P.

In FIG. 35 shows a SER characteristic for one embodiment of an interpolation method for various orders of DIDO and L ₀ = 16.

In FIG. 36 illustrates one embodiment of a system configured to utilize superclusters, DIDO clusters, and user clusters.

In FIG. 37 illustrates a system with user clusters in accordance with one embodiment of the present invention.

In FIG. 38a-b show thresholds of link quality metrics used in one embodiment of the present invention.

In FIG. 39-41 are examples of link quality matrices for organizing user clusters.

In FIG. 42 shows an embodiment in which a client moves through various DIDO clusters.

In FIG. 43-46 show the relationship between the resolution of spherical antenna arrays and their area A, in accordance with one embodiment of the present invention.

In FIG. 47 shows the degrees of freedom of MGMO systems in real-world indoor and outdoor distribution scenarios.

In FIG. 48 shows the degrees of freedom in DIDO systems depending on the diameter of the grating.

In FIG. 49 illustrates one embodiment of the present invention, including a plurality of centralized processors (CPs) and distributed nodes (DNs) exchanging information via a wired link or wireless connection.

In FIG. 50 illustrates one embodiment in which CPs exchange control data with unlicensed DNs and change their configuration so that they stop operating on frequency bands reserved for licensed use.

In FIG. 51 illustrates one embodiment of the present invention in which the entire spectrum is assigned to a new service, CP uses control data to disable all unlicensed DNs to avoid interference with licensed DNs.

In FIG. 52 illustrates one embodiment of a cloud-based wireless system including multiple CPs, distributed nodes, and a network connecting CPs and DNs.

In FIG. 53-59 illustrate one embodiment of the present invention with a multi-user (MU) multi-antenna system (MAS) adaptively changing the configuration of parameters to compensate for the Doppler effect resulting from user movement or changes in the distribution environment.

In FIG. 60, many base transceiver stations (BTS) are presented, some of which have a good signal-to-noise ratio (SNR), and some have low Doppler bias due to subscriber equipment.

In FIG. 61 shows one embodiment of a matrix containing SNR and Doppler bias values recorded by CPs for a plurality of BTS-UE links.

In FIG. 62 shows channel gain (or CSI) at various points in time in accordance with one embodiment of the present invention.

Detailed description

One solution to overcome many of the aforementioned limitations of the prior art is to implement the distributed input technology - distributed output (DIDO). DIDO technology is described in the following patents and patent applications, which are all assigned to the assignee of this patent and are incorporated herein by reference. This application is a partial continuation (CIP) of the applications listed below. These patents and applications are sometimes described together herein as “related patents and applications”:

US Patent Application No. 13 / 232,996, filed September 14, 2011, "Systems and Methods for Detecting Coherence Areas in Wireless Systems."

Application for US patent No. 13/233,006, filed September 14, 2011, "Systems and methods for the planned development and obsolescence of the multi-user spectrum."

US patent application No. 12/917,257, filed November 1, 2010, "Systems and methods for coordinating transmissions in distributed wireless systems through user clustering."

US Patent Application No. 12 / 802,988, filed June 16, 2010, “Interference Management, Handoff Strategy, Power Management, and Link Adaptation in Distributed Input-Distributed Output (DIDO) Communication Systems”.

US Patent Application No. 12 / 802,976, filed June 16, 2010, “System and Method for Optimizing Noise Reduction in DIDO Based on Signal Strength Measurement”.

US Patent Application No. 12 / 802,974, filed June 16, 2010, “A System and Method for Managing Cluster Relay Relays of Clients Passing Through Multiple DIDO Clusters.”

US Patent Application No. 12 / 802,989, filed June 16, 2010, “A system and method for controlling client handoffs between different distributed input / distributed output (DIDO) networks based on measured client speed.”

Application for US patent No. 12 / 802,958, filed June 16, 2010, "System and method for controlling power and grouping of antennas in networks with distributed input - distributed output (DIDO)".

US Patent Application No. 12 / 802,975, filed June 16, 2010, “System and Method for Adapting Communication Lines in Multi-Carrier DIDO Systems”.

US Patent Application No. 12 / 802,938, filed June 16, 2010, “System and Method for Interpolation of Pre-Coding in Multi-Carrier DIDO Systems”.

US patent application No. 12/630,627, filed December 2, 2009, "System and method for wireless communication with distributed antennas."

US patent 7,599,420, application filed August 20, 2007, issued October 6, 2009, "System and method for wireless communication with distributed input - distributed output."

US patent 7,633,994, application filed August 20, 2007, issued December 15, 2009, "System and method for wireless communication with distributed input - distributed output."

US patent 7,636,381, application filed August 20, 2007, issued December 22, 2009, "System and method for wireless communication with distributed input - distributed output."

Application for US patent No. 12/143,503, filed June 20, 2008, "System and method for wireless communication with distributed input - distributed output."

Application for US patent No. 11/256,478, filed October 21, 2005, "System and method for communication with spatially multiplexed tropospheric scattering."

US patent 7,418,053, application filed July 30, 2004, issued August 26, 2008, "System and method for wireless communication with distributed input - distributed output."

Application for US patent No. 10/817,731, filed April 2, 2004, "System and method for improving the quality of communication through waves with almost vertical ionospheric reflection (NVIS) using space-time coding."

In order to reduce the size and complexity of the present patent application, some related patents and applications are not explicitly disclosed here. For a detailed description of the respective inventions, refer to related patents and applications.

It should be noted that the following section I (Disclosure from related application No. 12 / 802,988) uses its own set of footnotes related to the prior art and prior applications assigned to the assignee of this application. References in footnotes are listed at the end of section I (immediately before the title of section II). The references used in Section II may have numerical designations that partially coincide with the references used in Section I, even though these numerical designations identify the various references (listed at the end of section II). Thus, links identified by a specific numerical designation can be identified within the section in which the numerical designation is used.

I. Disclosure from Related Application No. 12 / 802,988

1. Methods for removing intercluster interference

The following describes a wireless radio frequency (RF) communication system and methods comprising a plurality of distributed transmit antennas to create spaces in a space with zero RF energy. When using M transmit antennas, you can create up to (M-1) points with zero RF energy at predetermined locations. In one embodiment of the present invention, the zero RF energy points are wireless devices, and the transmit antennas obtain channel state information (CSI) between transmitters and receivers. In one embodiment, the receivers compute the CSI and return the information back to the transmitters. In another embodiment, the transmitters calculate CSI through training signals from the receivers, assuming the use of channel reversibility. Transmitters can use CSI to identify interference signals that will be transmitted simultaneously. In one embodiment, the transmit antennas use block diagonalization (BD) precoding to generate points with zero RF energy.

The system and methods described herein are different from the standard transmission / reception beamforming techniques described above. In fact, when beamforming during reception, weights are calculated to suppress interference on the receiving side (by controlling the zero position), while some embodiments of the present invention described herein apply weights on the transmitting side to create interference patterns resulting in one or many places in space with “zero RF energy”. Unlike standard beamforming for transmitting or precoding BDs designed to maximize signal quality (or SINR) for each user or downlink bandwidth, respectively, the systems and methods described herein minimize signal quality under certain conditions and / or from certain transmitters, thus creating points with zero RF energy in the client devices (sometimes referred to as “users” here). In addition, in the context of Distributed Input - Distributed Output (DIDO) systems (described in related patents and applications), transmit antennas distributed in space provide higher degrees of freedom (i.e., higher spatial channel diversity) that can be applied. to create multiple points with zero RF energy and / or to obtain maximum SINR for various users. For example, using M transmit antennas, you can create up to (M-1) RF energy points. In contrast, in practice, beamforming or multi-user systems with BDs are typically designed with closely spaced antennas on the transmitting side, which limits the number of simultaneous users that can be served over the wireless link for any number of transmitting antennas M.

Consider a system with M transmit antennas and K users, with K <M. We assume that the transmitter receives CSI (H∈C ^{K × M} ) between M transmit antennas and K users. To simplify, we assume that each user has one antenna, but the same method can be extended to the case of multiple receiving antennas for each user. Pre-coding weights (w∈C ^{M × 1} ) creating zero RF energy at K user locations are calculated to satisfy the following condition:

Hw = 0 ^{K × 1} ,

where 0 ^{K × 1} is a vector with all zero elements, and H is a channel matrix obtained by combining channel vectors (h _k ∈C ^{1 × M} ) from M transmitting antennas to K users as:

In one embodiment, the singular decomposition (SVD) of the channel matrix H is calculated, the precoding weight w is determined as the right singular vector corresponding to the zero subspace (identified by the zero singular value) for H.

Transmitting antennas use the weight vector defined above to transmit RF energy, while at the same time creating K points with zero RF energy at the locations of K users, so that the signal received by the k-th user is expressed by the equation:

r _k = h _k ws _k ÷ n _k = 0 ÷ n _k ,

where n _k ∈C ^{1 × 1} is the additive white Gaussian noise (AWGN) of the kth user.

In one embodiment, the singular decomposition (SVD) of the channel matrix H is calculated, the precoding weight w is determined as the right singular vector corresponding to the zero subspace (identified by the zero singular value) for H.

In another embodiment, the wireless system is a DIDO system, and zero RF energy points are provided to pre-suppress interference between clients between different DIDO coverage areas. US Patent Application No. 12 / 630,627 describes a DIDO system including:

- DIDO customers;

- DIDO distributed antennas;

- base transceiver stations (BTS) DIDO;

- Base Station Network (BSN) DIDO.

Each BTS is connected via a BSN to a plurality of distributed antennas providing service in a particular coverage area called a DIDO cluster. In this patent application, we describe a system and method for suppressing interference between adjacent DIDO clusters. As shown in FIG. 1, we assume that a client is located in the main DIDO cluster (i.e., a user device serviced by the DIDO multi-user system), which is affected by interference (or the target client) from neighboring clusters.

In one embodiment, neighboring clusters operate at different frequencies according to frequency division multiple access (FDMA) technologies, like standard cellular systems. For example, with a frequency reuse factor of 3, every third DIDO cluster reuses the same carrier frequency, as shown in FIG. 2. In FIG. 2 other carrier frequencies are indicated as F ₁ , F ₂ and F ₃ . Although this embodiment can be applied in some implementations, such a solution ultimately leads to a decrease in spectral efficiency, since the available spectrum is divided into many subbands, and only one subset of the DIDO clusters operates in one subband. In addition, to prevent interference, complex cellular network planning is required to connect different DIDO clusters with different frequencies. Like prior art cellular systems, such cellular planning requires special placement of antennas and limitation of transmit power in order to avoid interference between clusters using the same frequency.

In another embodiment, neighboring clusters operate in the same frequency band, but in different time slots in accordance with time division multiple access (TDMA) technology. For example, as shown in FIG. 3, DIDO transmission is allowed only at time points T ₁ , T ₂ and T ₃ for specific clusters. Temporary slots can be assigned equally to different clusters, so that different clusters are planned in accordance with the circular service policy. If different clusters characterize different requirements for data transfer rates (i.e. clusters in crowded urban environments compared to clusters in rural areas with fewer clients per coverage area), then different priorities are assigned to different clusters, so more time slots are assigned to clusters with high requirements for data transfer speed. Although TDMA, as described above, can be applied in one embodiment of the present invention, the TDMA approach may require time synchronization of different clusters and may lead to lower spectral efficiency, since interfering clusters cannot use the same frequency at the same time.

In one embodiment, all neighboring clusters transmit simultaneously in the same frequency band and use spatial processing for the clusters to avoid interference. In the present embodiment, the DIDO multicluster system: (i) uses standard DIDO precoding inside the main cluster to transmit simultaneous, non-interfering data streams within the same frequency band to multiple clients (as described in related patents and applications, including US patents 7,599,420; 7,633,994; 7,636,381; and application No. 12 / 143,503); (ii) uses DIDO precoding with interference cancellation in neighboring clusters to avoid interference with clients located in the interference zones 8010, as shown in FIG. 4, creating points with zero radio frequency (RF) energy at the locations of the target customers. If the target client is in the interfering zone 410, it will receive the sum of the RF energy containing the data stream from the main cluster 411, and zero RF energy from the interfering cluster 412-413, which will simply be RF energy containing the data stream from the main a cluster. Thus, adjacent clusters can use the same frequency at the same time, without having targeted clients suffering from interference in the interfering zone.

In real systems, various factors can influence the performance of DIDO precoding, such as: channel estimation error or Doppler effects (leading to outdated channel status information on distributed DIDO antennas); crosstalk (IMD) in multi-carrier DIDO systems; time or frequency offsets. As a result of the influence of these effects, reaching points with zero RF energy may not be practical. However, while the RF energy of the target client from the interfering clusters is negligible compared to the RF energy from the main cluster, the interference does not affect the communication line characteristics of the target client. For example, suppose a client requires a 20 dB signal-to-noise ratio (SNR) to demodulate a 4-QAM combination using forward error correction coding (FEC) to achieve a target error bit rate (BER) of 10 ^-6 . If the RF energy from the target client received from the interfering cluster is 20 dB lower than the RF energy received from the main cluster, the interference is negligible and the client can successfully demodulate data within the specified BER target value. Thus, the term “zero RF energy” as used herein does not necessarily mean that the RF energy of the RF interference signals is zero. Rather, it means that the RF energy is quite low relative to the RF energy of the useful RF signal, so that the receiver can receive the useful RF signal. Furthermore, although certain desired threshold values for the useful RF energy are described for the RF interference energy, the fundamental principles of the invention are not limited to any particular threshold values.

There are various types of interfering zones 8010, as shown in FIG. 4. For example, the “type A” zones (indicated by the letter “A” in Fig. 80) are affected by interference from only one neighboring cluster, while the “type B” zones (indicated by the letter “B”) take into account interference from two or more neighboring ones clusters.

In FIG. 5 shows the structure used in one embodiment of the present invention. Dots indicate distributed DIDO antennas, crosses indicate DIDO customers, and arrows indicate directions of propagation of RF energy. The DIDO antennas in the primary cluster transmit precoded data signals to MS 501 clients in that cluster. Similarly, the DIDO antennas in the interfering cluster serve IC 502 clients within that cluster through standard DIDO precoding. The green cross 503 denotes the target client TC 503 in the area that creates interference. The DIDO antennas in the main cluster 511 transmit precoded data signals to the target client (black arrows) using standard DIDO precoding. The DIDO antennas in the interfering cluster 512 use precoding to generate zero RF energy in the direction of the target client 503 (green arrows).

The signal received by the target client k in any interfering area 410A, B in FIG. 4 is described by the equation:

where k = 1, ..., K, and K is the number of clients in the interfering zone 8010A, B, U is the number of clients in the main DIDO cluster, C is the number of interfering clusters 412-413 DIDO and I _c is the number of clients in the interfering cluster c. In addition, r _k ∈C ^{N × M} is a vector containing received data streams by client k under the assumption that there are M DIDO transmit antennas and N receive antennas in the client devices; s _k ∈C ^{N × 1} is the vector of transmitted data streams to client k in the main DIDO cluster; s _u ∈C ^{N × 1} is the vector of transmitted data streams to the client and in the main DIDO cluster; s _{c, i} ∈C ^{N × 1} is the vector of transmitted data streams to client i in the c-th interfering DIDO cluster; n _k ∈C ^{N × 1} is the additive white Gaussian noise (AWGN) vector in the N receiving antennas of client k; H _k ∈C ^{N × M} is the DIDO channel matrix from M transmitting DIDO antennas to N receiving antennas at client k in the main DIDO cluster; H _{c, k} ∈C ^{N × M} is the DIDO channel matrix from M DIDO transmit antennas to N receive antennas at client k in the c-th DIDO interfering cluster; W _k ∈C ^{M × N} is the DIDO precoding weight matrix for client k in the main DIDO cluster; W _k ∈C ^{M × N} - DIDO precoding weight matrix for client u in the main DIDO cluster; W _{c, i} ∈C ^{M × N} is the DIDO precoding weight matrix for client i in the cth DIDO interfering cluster.

To simplify the conventions without loss of generality, we assume that all clients are equipped with N receiving antennas and in each DIDO cluster there are M distributed DIDO antennas, with M≥ (N⋅U) and M≥ (N⋅I _c ), ∀c = 1 , ..., C. If M is greater than the total number of receive antennas in the cluster, additional transmit antennas are used to pre-suppress interference with target clients in the interfering area, or to improve the reliability of the communication line for clients within the same cluster through diversity schemes, described in related pa tents and applications, including in US patents 7,599,420; 7,633,994; 7,636,381 and application No. 12/143,503.

DIDO precoding weights are calculated to pre-suppress inter-client interference within a single DIDO cluster. For example, precoding with block diagonalization (BD) described in related patents and applications, including in US patents 7,599,420; 7,633,994; 7,636,381 and application No. 12/143,503 and [7], can be used to remove inter-client interference so that the following condition is satisfied in the main cluster:

The precoding weight matrices in adjacent DIDO clusters are designed to meet the following conditions:

To calculate the precoding matrices W _{c, i} , the downstream channel from the M transmit antennas to I _c clients in the interfering cluster is evaluated, as well as to the client k in the interfering zone, and the precoding matrix is calculated on the BTS DIDO in the interfering cluster. If the BD method is used to calculate precoding matrices in interfering clusters, the following effective channel matrix is created to calculate the weights for the ith client in neighboring clusters:

- матрица, полученная из матрицы канала

для создающего помехи кластера с, где строки, соответствующие i-му клиенту, удалены.Where

- matrix obtained from the channel matrix

for the interfering cluster c, where the rows corresponding to the i-th client are deleted.

Substituting conditions (2) and (3) into (1), we obtain the received data streams for the target client k, in which the intracluster and intercluster interference are removed.

The precoding weights W _{c, i} in (1) calculated in neighboring clusters are calculated with the possibility of transmitting precoded data streams to all clients in these clusters, while at the same time suppressing the interference for the target client in the interference zone in advance. The target client receives precoded data only from its primary cluster. In another embodiment, the same data stream is sent to the target client from both the primary and neighboring clusters to obtain diversity gain. In this case, the signal model in (5) is expressed as follows:

where W _{c, k} is the DIDO precoding matrix from the DIDO transmitters in the cth cluster to the target client k in the interfering area. It should be noted that the method in (6) requires time synchronization of neighboring clusters, which can be difficult to achieve in large systems, but, nevertheless, it is quite justified if the advantage of diversity gain justifies implementation costs.

We start by evaluating the characteristics of the proposed method in terms of the frequency of occurrence of erroneous symbols (SER) depending on the signal-to-noise ratio (SNR). Without loss of generality, we determine the following signal model, taking one antenna for each client, and reformulate (1) as follows:

where INR is the noise-to-noise ratio, defined as INR = SNR / SIR, and SIR is the signal-to-noise ratio.

In FIG. Figure 6 shows the dependence of SER on SNR, taking SIR = 10 dB, for the target client in the interfering area. Without loss of generality, we measured SER for 4-QAM and 16-QAM without forward error correction coding (FEC). We got a target SER of 1% for non-encoded systems. This target value corresponds to different SNR values depending on the modulation order (i.e. SNR = 20 dB for 4-QAM and SNR = 28 dB for 16-QAM). Lower target SER values can be satisfied with the same SNR values using FEC coding due to the gain provided by the coding. We are considering a scenario of two clusters (one main cluster and one interfering cluster) with two DIDO antennas and two clients (each of which is equipped with one antenna) per cluster. One of the clients in the primary cluster is in the interfering area. We consider narrow-band channels with shallow fading, but the following results can be extended to frequency-selective multi-carrier systems (OFDM), where each subcarrier undergoes shallow fading. We consider two scenarios: (i) one — with interference between DIDO clusters (IDCI), where precoding weights w _{c, i} are calculated without considering the target client in the interfering area; and (ii) another in which the IDCI is removed by calculating the weights w _{c, i} to destroy the IDCI for the target client. We observe that in the presence of IDCI, the SER value is high and exceeds the target. When precoding with IDCI in a neighboring cluster, interference to the target client is suppressed, and SER targets are achieved for SNR> 20 dB.

The results presented in FIG. 6 suggest precoding with IDCI, as in expression (5). If precoding with IDCI is also used in neighboring clusters for precoding data streams for the target client in the interfering area, as in expression (6), diversity provides an additional gain. In FIG. 7 shows a comparison of SER obtained in two ways: (i) “Method 1”, which uses precoding with IDCI according to (5); (ii) “Method 2”, which uses precoding with IDCI according to (6), where the neighboring clusters also transmit a stream of precoded data to the target client. Method 2 provides a gain of ~ 3 dB compared to standard precoding with IDCI due to the additional gain provided by the DIDO antennas in the neighboring cluster used to transmit the precoded data stream to the target client. In a more general sense, the gain due to the matrix in method 2 relative to method 1 is proportional to 10 * log10 (C + 1), where C represents the number of neighboring clusters, and the coefficient “1” denotes the main cluster.

Then we evaluate the characteristics of the above method, depending on the location of the target client relative to the area that creates the interference. We consider one simple scenario in which the target client 8401 moves from the main DIDO cluster 802 to the interfering cluster 803, as shown in FIG. 8. We assume that all 812 DIDO antennas within the main 802 cluster use BD precoding to suppress intra-cluster interference and satisfy condition (2). We assume the only DIDO interfering cluster, the only receiving antenna in the client device 801, and equal losses in the signal path from all DIDO antennas in the main or interfering cluster to the client (i.e., to DIDO antennas arranged in a circle around the client). We apply a simplified path loss model with an exponential of 4 path losses (as in typical urban environments) [11].

The subsequent analysis is based on the following simplified signal model, expanding (7) to account for losses along the signal path:

where the signal-to-noise ratio (SIR) is calculated as SIR = ((1-D) / D) ⁴ . When modeling IDCI, we consider three scenarios: i) an ideal case without IDCI; ii) pre-suppression of IDCI by precoding BD in the interfering cluster to fulfill condition (3); iii) with IDCI, without prior suppression by a neighboring cluster.

In FIG. 9 shows the signal-to-noise interference-to-noise ratio (SINR) versus D (i.e., when the target client moves from the main cluster 802 in the direction of the DIDO antennas 813 in the interfering cluster 8403). SINRs are calculated as the ratio of signal powers to a mixture of interference and noise using the signal model presented in (8). In the absence of IDCI, only noise affects the characteristics of the wireless link, and SINR is reduced due to path loss. In the presence of IDCI (i.e., without precoding with IDCI), interference from DIDO antennas in a neighboring cluster helps to reduce SINR.

In FIG. 10 shows a characteristic of the frequency of occurrence of erroneous symbols (SER) for the above three scenarios for modulating 4-QAM in narrow-band channels with shallow fading. These SER results correspond to the SINR shown in FIG. 9. We assume that the SER threshold value is 1% for non-encoded systems (ie, without FEC), which corresponds to the SINR threshold value, SINR _T = 20 dB, shown in FIG. 9. The SINR threshold depends on the modulation order used for data transmission. Higher modulation orders are typically characterized by a higher SINR _T , allowing to achieve the same target error rate. With FEC, a lower target SER can be achieved for the same SINR due to the gain from coding. In the case of IDCI without precoding, the target SER is reachable only within the range of D <0.25. When precoding with IDCI in a neighboring cluster, the range satisfying the target SER is expanded to D <0.6. Above this range, the SINR is increasing due to path loss, and the target SER is not reached.

One embodiment of an IDCI precoding method is shown in FIG. 11 and consists of the following stages.

- SIR score 1101. Clients evaluate the signal strength from the main DIDO cluster (ie based on the received precoded data) and the signal strength “interference-to-noise mix” from neighboring DIDO clusters. In single-carrier DIDO systems, a frame structure with short silence periods can be developed. For example, silence periods can be set between using a training signal for channel estimation and transmitting precoded data during feedback to transmit channel status information (CSI). In one embodiment, the power of the “interference-to-noise mix” signal from neighboring clusters is measured during silent periods from DIDO antennas in the main cluster. In real-world DIDO multi-carrier (OFDM) systems, zero tones are typically used to prevent direct current (DC) bias and attenuation at the edge of the band due to filtering at the transmit and receive sides. In another embodiment using multi-carrier systems, the signal strength of the “interference-to-noise mix” is estimated from zero tones. Correction factors can be used to compensate for the attenuation of transmit / receive filters at the edge of the band. When the power of the “interference-to-noise” signal mixture (P _S ) from the main cluster and the “noise-to-noise” mixture power from neighboring clusters (P _IN ) are estimated, the client calculates the SINR as follows:

In an alternative embodiment, a SINR estimate is obtained from a received signal strength indication (RSSI) used in typical wireless communication systems for measuring radio signal strength.

It should be noted that the indicator in (9) cannot distinguish between the noise power level and the interference power level. For example, customers who are affected by shading (i.e., those behind obstacles that weaken the signal power from all distributed DIDO antennas in the main cluster) in interference-free environments may receive a low SINR even if there is no intercluster interference.

A more reliable indicator for the proposed method is the SIR value, calculated as follows:

where P _N is the noise power. In real OFDM systems with several carriers, the noise power P _N in (10) is estimated from zero tones, assuming that all DIDO antennas from the main and neighboring clusters use the same set of zero tones. The power of the noise-to-noise (P _IN ) mixture is evaluated during a period of silence, as mentioned above. Finally, the power (P _S ) of the “signal with interference and noise” mixture is obtained from the tonal data signals. Based on these estimates, the client calculates the SIR value in expression (10).

- Channel estimation in neighboring clusters 1102-1103. If the estimated SIR value in (10) is below a predetermined threshold (SIR _T ) determined in step 8702 of FIG. 11, the client begins to listen to training signals from neighboring clusters. It should be noted that the SIR _T value depends on the modulation coding scheme and FEC (MCS) used for data transmission. Different target SIR values are determined depending on the MCS at the client. When the distributed DIDO antennas of the different clusters are time synchronized (ie, tied to the same pulse per second, PPS, counting time), the client uses the control sequence to transmit its channel estimates to the DIDO antennas in neighboring clusters at step 8703. The control sequence To estimate the channel in neighboring clusters, it was designed orthogonally to the control sequence from the main cluster. In an alternative embodiment, when the DIDO antennas in different clusters are not time synchronized, orthogonal sequences (with good cross-correlation properties) are used for time synchronization in different DIDO clusters. Once the client is tied to the reference time / reference frequency of neighboring clusters, at 1103, a channel estimate is performed.

- Pre-coding with IDCI 1104. As soon as channel estimates are available on BID DIDOs in neighboring clusters, precoding with IDCI is computed to satisfy the condition in expression (3). DIDO antennas in neighboring clusters transmit precoded data streams only to clients in their cluster, while at the same time pre-suppressing interference with clients in the interfering area 410 of FIG. 4. It should be noted that if the client is in the interfering area 410 of type B of FIG. 4, multiple clusters generate interference with the client, and all neighboring clusters perform precoding with IDCI simultaneously.

Handover Methods

Next, we describe various handoff methods for clients moving through DIDO clusters filled with distributed antennas located in separate areas or providing various types of services (i.e. low or high mobility services).

but. Handoff between adjacent clusters

DIDO In one embodiment, the IDCI precoder for removing intercluster interference described above is used as a starting point for handoff methods in DIDO systems. The standard handoff in cellular systems is designed to seamlessly switch clients between cells served by various base stations. In systems, DIDO handoff allows clients to move from one cluster to another without losing a connection.

To illustrate one embodiment of a handoff strategy for DIDO systems, we again look at the example in FIG. 8 with only two clusters 802 and 803. As the client 801 moves from the main cluster (C1) 802 to the neighboring cluster (C2) 803, in one embodiment of the handoff method, the signal quality in different clusters is dynamically calculated and a cluster is selected, leading to the lowest error rate for the client.

In FIG. 12 shows the change in SINR depending on the distance between the client and the center of the C1 clusters. For 4-QAM modulation without FEC coding, we take the target value SINR = 20 dB. The line shown by the circles represents the SINR for the target client served by the DIDO antennas in C1 when both C1 and C2 use DIDO precoding without interference cancellation. The SINR value decreases as a function of D due to losses in the signal path and interference from a neighboring cluster. When precoding with IDCI is implemented in the neighboring cluster, the SINR reduction occurs only due to losses in the signal path (as shown by the line with triangles), since the interference is completely removed. Symmetric behavior occurs when a client is served by a neighboring cluster. One embodiment of a handoff strategy is determined such that as the client moves from C1 to C2, the algorithm switches between different DIDO schemes to maintain the SINR value above a predetermined target value.

From the graphs in FIG. 12, we obtain the SER value for 4-QAM modulation in FIG. 13. It should be noted that switching between different precoding strategies maintains the SER value within the specified target value.

One embodiment of a handoff strategy is described below.

- Pre-coding with C1-DIDO and C2-DIDO. When the client is within C1, away from the interfering zone, both clusters, C1 and C2, work with standard DIDO precoding independently.

- Pre-coding with C1-DIDO and C2-IDCI. As the client moves in the direction of the interfering zone, the SIR or SINR values deteriorate. When the target SINR _{T1 is} reached, the target client begins to estimate the channel from all DIDO antennas in C2 and provides CSI on the BTS in C2. The BTS in C2 calculates precoding with IDCI and transmits to all clients in C2, while preventing interference with the target client. While the target client is in the interfering area, it will continue to provide its CSI for both C1 and C2.

- Pre-coding with C1-IDCI and C2-DIDO. As the client moves in direction C2, its SIR or SINR value continues to fall until it again reaches the target value. At this point, the client decides to switch to a neighboring cluster. In this case, C1 starts using CSI from the target client to create zero interference in its direction using precoding with IDCI, while the neighboring cluster uses CSI for standard DIDO precoding. In one embodiment, as the SIR estimate approaches the target value, clusters C1 and C2 alternatively try both DIDO and IDCI precoding schemes to allow the client to evaluate the SIR for both cases. The client then selects the best scheme to maximize a specific indicator of the error rate. When this method is applied, the transition point for the handover strategy is at the intersection of the curves with triangles and rhombuses in FIG. 12. One embodiment is configured to use the modified IDCI precoding method described in (6), in which a neighboring cluster also transmits a stream of precoded data to the target client to provide gains due to the grating. With this approach, the handoff strategy is simplified since the client does not need to evaluate the SINR for both strategies at the transition point.

- Pre-coding with C1-DIDO and C2-DIDO. As the client moves from the interfering zone in the direction of C2, the main cluster C1 stops pre-suppressing the interference in the direction of this target client through precoding with IDCI and switches back to the standard DIDO precoding for all clients remaining in C1. This final transition point in our handover strategy is suitable to avoid unnecessary CSI feedback from the target client to C1, which reduces overhead on the feedback channel. In one embodiment, a second target SINR _T2 value is determined. When the SINR (or SIR) value exceeds this target value, the strategy switches to C1-DIDO and C2-DIDO. In one embodiment, cluster C1 continues to switch between DIDO precoding and IDCI to allow the client to evaluate SINR. The client then selects the method for C1 that is closest to the target SINR _T1 value indicated above.

The method described above calculates the SINR or SIR estimates for the various schemes in real time and uses them to select the optimal scheme. In one embodiment, the handoff algorithm is developed based on the state machine shown in FIG. 14. The client monitors its current state and switches to the next state when the SINR or SIR value passes below or above the predetermined threshold values shown in FIG. 12. As discussed above, in state 1201, both clusters C1 and C2 independently operate with standard DIDO precoding, and client C1 is served by cluster C1; in client state 1202, serves cluster C1, the BTS in C2 calculates precoding with IDCI, and cluster C1 operates using standard DIDO precoding; in client state 1203, it serves cluster C2, the BTS in C1 calculates precoding with IDCI, and cluster C2 operates using standard DIDO precoding; and in client state 1204, it serves cluster C2, and both clusters, C1 and C2, independently operate with standard DIDO precoding.

In the presence of shading effects, the signal quality or SIR may fluctuate around thresholds, as shown in FIG. 15, causing repeated switching between successive states shown in FIG. 14. A repeated change of state is an undesirable effect, as it leads to significant overhead on the control channels between the clients and the BTS to allow switching between transmission schemes. In FIG. 15 shows an example of a handoff strategy in the presence of shading. In one embodiment, the shading factor is modeled according to the normal logarithmic distribution with dispersion 3 [3]. Next, we identify some ways to prevent the effect of repeated switching during DIDO handoff.

One embodiment of the invention is configured to use a hysteresis loop to combat state switching effects. For example, when switching between the states “C1-DIDO, C2-IDCI” 9302 and “C1-IDCI, K2-DIDO” 9303 in FIG. 14 (or vice versa) the threshold value of SINR _T1 can be adjusted within the range of A ₁ . This method avoids repeated switching between states when signal quality fluctuates around SINR _T1 . For example, in FIG. 16 shows the hysteresis loop mechanism when switching between any two states shown in FIG. 14. To switch from state B to state A, the SIR value must exceed (SIR _T1 + A _1/2 ), but to switch back from state A to state B, the SIR value must fall below (SIR _T1 -A _1/2 ).

In another embodiment, the threshold SINR _{T2 is} adjusted to avoid repeated switching between the first and second (or third and fourth) states of the state machine shown in FIG. 14. For example, the range of A ₂ values can be determined such that the threshold value SINR _{T2 is} selected within this range depending on the channel mode and the shading effects.

In one embodiment, depending on shading changes expected on the wireless link, the SINR threshold is dynamically adjusted within the range of [SINR _T2 , SINR _T2 + A ₂ ]. The variance of the log-normal distribution can be estimated by the variance of the received signal power (or RSSI) as the client moves from its current cluster to a neighboring cluster.

The methods described above assume that the client has launched a handoff strategy. In one embodiment, the handoff decision is forwarded to DIDO BTSs under the assumption that communication through multiple BTSs is allowed.

To simplify the above methods are obtained without taking into account the encoding of FEC and 4-QAM. More generally, SINR or SIR thresholds are obtained for various modulation coding schemes (MCS), and a handover strategy is being developed in conjunction with link adaptation (see, for example, US Pat. No. 7,636,381) to optimize downlink data rate to each client in the interfering area.

b. Handoff between DIDO low and high Doppler networks

DIDO systems are configured to utilize feedback transmission schemes for precoding downlink data streams. Feedback circuits have a limitation associated with the delay in the feedback channel. In real DIDO systems, the computation time can be reduced with high-power transceivers and it is expected that most of the delay is introduced by the BSN DIDO when delivering CSI and precoded baseband data from the BTS to the distributed antennas. The BSN may contain various network technologies, including without limitation digital subscriber lines (DSL), cable modems, fiber optic rings, T1 lines, hybrid fiber coaxial (HFC) networks and / or fixed wireless networks (e.g. WiFi). Dedicated fiber lines typically have very high bandwidth and low latency, potentially less than one millisecond in the local area, but they are less common than DSL and cable modems. Today DSL and cable modem connections typically have a delay of 10-25 ms at the “last mile” in the United States, but they are very widespread.

The maximum delay in the BSN determines the maximum Doppler frequency allowed on the DIDO wireless link without degrading the DIDO precoding performance. For example, in [1], we showed that at a carrier frequency of 400 MHz, networks with a delay of approximately 10 ms (ie, DSL) can allow client speeds of up to 3.58 m / s (8 mph) (running speed) while networks with a 1 ms delay (i.e., fiber optic ring) can support speeds of up to 31.3 m / s (70 mph) (i.e. freeway traffic).

We define two or many DIDO subnets based on the maximum Doppler frequency allowed in the BSN. For example, BSNs with high latency DSL connections between DIDO BTSs and distributed antennas can only provide low mobility services or fixed wireless services (i.e., low Doppler network), while low latency BSNs on a low latency fiber optic ring can provide highly mobile services (i.e., high Doppler network). We see that most broadband users do not move when using broadband, and, moreover, most of them are unlikely to be located near areas with many objects moving at high speed (for example, near a high-speed highway), since such places are usually less desirable for living or working in an office. However, there are broadband users who use broadband at high speeds (for example, when the car is traveling on the highway) or located near high-speed objects (for example, in a warehouse located near the highway). To solve the situation of these two different scenarios of the Doppler effect for users in one embodiment, the low-Doppler DIDO network usually consists of a larger number of DIDO antennas with relatively low power (i.e., 1-100 W for indoor or on-roof installation) area, while a high-Doppler network typically consists of fewer DIDO antennas with high power transmission (i.e. 100 watts for installation on a roof or tower). A low-Doppler DIDO network typically serves a large number of low-Doppler users and can do this, usually at a lower cost, using low-cost, high-latency, high latency connections such as DSL and cable modems. A high-Doppler DIDO network typically serves a smaller number of high-Doppler users and can do this, as a rule, at a higher communication cost, using more expensive low-latency broadband connections such as fiber.

In order to avoid interference among various types of DIDO networks (e.g., Low Doppler or High Doppler), various multiple access technologies such as time division multiple access (TDMA), frequency division multiple access (FDMA), or code division multiple access (CDMA) can be applied .

Further, we offer ways to assign clients to various types of DIDO networks and enable handoff between them. The choice of network is based on the type of mobility of each client. Client speed (ν) is proportional to the maximum Doppler shift according to the following equation [6]:

where f _d is the maximum Doppler shift, λ is the wavelength corresponding to the carrier frequency, and θ is the angle between the vector indicating the direction of the client-transmitter and the velocity vector.

In one embodiment, the Doppler bias of each client is calculated by blind estimation methods. For example, Doppler shift can be estimated by sending RF energy to the client and analyzing the reflected signal, similar to Doppler radar systems.

In another embodiment, one or multiple DIDO antennas send training signals to the client. Based on these training signals, the client estimates the Doppler shift using techniques such as counting the intersections of the channel gain level zero or performing spectral analysis. It should be noted that for a fixed velocity ν and a client trajectory, the angular velocity υ sin θ in (11) may depend on the relative distance of the client to each DIDO antenna. For example, DIDO antennas near a moving client result in greater angular velocity and Doppler shift than remote antennas. In one embodiment, the Doppler speed is estimated from a plurality of DIDO antennas at various distances from the client, and the average value, weighted average value or standard deviation is used as an indicator of client mobility. Based on the evaluated Doppler indicator, BTS DIDO decides whether to assign a client a low Doppler or high Doppler network.

The Doppler indicator is periodically monitored for all clients, and its data is sent back to the BTS. When one or many clients change their Doppler speed (i.e., a client traveling on a bus, or a client walking or standing still), these clients are dynamically reassigned to another DIDO network that can support their level of mobility.

Although the Doppler shift for low-speed clients can be affected by nearby high-speed objects (for example, close to the highway), the Doppler shift is usually much smaller than the Doppler shift of clients who are in independent movement. In this regard, in one embodiment, the client’s speed is estimated (for example, using tools such as monitoring the client’s position using GPS), and if the speed is low, the client is assigned a low Doppler network, and if the speed is high, the client is assigned a high Doppler network.

Methods for power control and antenna grouping

A block diagram of a power control DIDO system is shown in FIG. 17. One or many data streams (s _k ) for each client (1, ..., U) are first multiplied by the weights generated by the DIDO precoding unit. The precoded data streams are multiplied by a scaled power factor calculated by the power control unit based on the input channel quality information (CQI). CQI is either returned from clients to the BTS DIDO, or retrieved from the uplink, allowing uplink-downlink reversibility. Finally, the streams t _m are sent to a digital-to-analog converter (DAC) block, a radio frequency (RF) block, a power amplifier (PA) block, and finally to the antennas.

The power control unit measures CQI for all customers. In one embodiment, the CQI is an average SNR or RSSI. CQI varies for different clients depending on the path loss or shadowing. Our power control method adjusts the scaled transmission power factors P _k for various clients and multiplies them by the pre-encoded data streams generated for various clients. It should be noted that one or many data streams can be generated for each client depending on the number of receiving antennas of the clients.

To evaluate the characteristics of the proposed method, we determined the following signal model based on expression (5), including losses on the signal path and power control parameters:

where k = 1, ..., U, U is the number of clients, SNR = P _o / N _o , where P _o is the average transmit power, N _o is the noise power and α _k is the loss coefficient during signal transmission / shadowing. To model signal loss / shadowing, we use the following simplified model:

where a = 4 is the exponent of losses during the passage of the signal, and we believe that the losses during the passage of the signal increase with an increase in the client index (i.e., clients are located at an increasing distance from DIDO antennas).

In FIG. Figure 18 shows the SER values as a function of SNR, assuming four DIDO transmit antennas and four clients in different scenarios. The ideal case assumes that all clients have the same losses along the signal path (i.e., a = 0), which leads to Pk = 1 for all clients. The square graph refers to the case when clients have different loss factors along the signal path and there is no power control. The curve with points is obtained from the same scenario (with losses on the signal path), in which the power control coefficients are chosen so that P _k = 1 / α _k . When applying the power control method, more power is assigned to data streams intended for clients experiencing higher signal loss / shadowing, which leads to an increase in SNR by 9 dB (for this specific scenario) compared to the case without power control.

The United States Federal Communications Commission (FCC) (and other international regulatory agencies) sets limits on the maximum power transmitted from wireless devices to limit the effects of electromagnetic (EM) radiation on the human body. There are two types of limit values [2]: i) “professional / controllable” limit value, when people are fully aware of the source of radio frequency (RF) radiation through fences, warnings or announcements; ii) the marginal value for “general population / uncontrollable” when there is no impact management.

Different types of wireless devices have different radiation levels. In general, DIDO distributed antennas used indoors / outdoors qualify under the FCC category as “mobile” devices, defined according to [2] as “transmitting devices designed to be used in places other than fixed locations, which are usually used with radiating structures held at a distance of 20 cm or more from the user's body or people nearby. "

EM radiation of “mobile” devices is measured from the point of view of the maximum permissible dose (SDA), expressed in mW / cm ² . In FIG. 19 shows the power density of traffic rules depending on the distance from the source of radio frequency radiation for various values of transmit power at a carrier frequency of 700 MHz. The maximum allowable power required to meet the “uncontrolled” FCC limit value for devices operating, as a rule, at a distance of more than 20 cm from the human body, is 1 W.

Less stringent restrictions on radiated power are defined for transmitters installed on roofs or buildings, away from the "ordinary population". For these “rooftop transmitters,” the FCC defines a less stringent radiation limit of 1000 watts, measured in terms of effective radiation power (ERP).

Based on the FCC limitations mentioned above, in one embodiment, we define two types of distributed DIDO antennas for real systems.

- Low-power (LP) transmitters: located anywhere (i.e. indoors or outdoors) at any height, with a maximum transmission power of 1 W and the ability to connect to a consumer-grade broadband transport network at a speed of 5 Mbps (for example, DSL, cable modem, Fibe To The Home (FTTH)).

- High-power (NR) transmitters: antennas mounted on roofs or on buildings at an altitude of about 10 meters, with a transmit power of 100 W and a commercial broadband transport communication network (for example, a fiber optic ring) with effectively “unlimited” data transfer speed compared to bandwidth available on DIDO wireless links.

It should be noted that LP transmitters with the ability to communicate with a DSL or cable modem are good candidates for low-Doppler DIDO networks (as described in the previous section), since their clients are mostly stationary or have low mobility. HP transmitters that can be connected to a commercial fiber optic link can allow higher client mobility and can be used in high-Doppler DIDO networks.

To gain practical knowledge of the characteristics of DIDO systems with various types of LP / HP transmitters, we consider a real case of installing a DIDO antenna in the center of Palo Alto, California. In FIG. 20a shows a random distribution of N _LP = 100 low-power distributed DIDO antennas in Palo Alto. In FIG. 20b 50 LP antennas are replaced by N _HP = 50 high-power transmitters.

Based on the distributions of the DIDO antennas in FIG. 20a-b, we get coverage maps in Palo Alto for systems using DIDO technology. In FIG. 21a and 21b show two power distributions corresponding to the configurations shown in FIG. 20a and FIG. 20b, respectively. The distribution of received power (expressed in dBm) is obtained by accepting the model of signal path / shadow path loss for urban environments, defined by the 3GPP standard [3], at a carrier frequency of 700 MHz. We see that using 50% HP transmitters leads to better coverage of the selected area.

In FIG. 22a-b show the velocity distribution for the two scenarios described above. Bandwidth (expressed in Mbps) is obtained based on threshold power values for various modulation coding schemes defined in the 3GPP (LTE) long-term development standard in [4, 5]. The total available bandwidth is set to 10 MHz at a carrier frequency of 700 MHz. Two different frequency allocation plans are considered: i) the 5 MHz spectrum is allocated only to LP stations; ii) 9 MHz is allocated to HP transmitters, and 1 MHz is allocated to LP transmitters. It should be noted that a lower bandwidth is usually allocated to LP stations because of their ability to connect to a transport network with limited bandwidth with DSL. In FIG. 22a-b show that when using 50% HP transmitters, the speed distribution can be significantly increased by increasing the average transmission rate for each client from 2.4 Mbps in FIG. 22a to 38 Mbps in FIG. 22b.

Then we determined the transmit power control algorithms for the LP stations, so that higher power is enabled at any given time, which increases the downstream throughput of DIDO systems, as shown in FIG. 22b. We see that the FCC limit values for power density are determined on the basis of the average value over time as follows [2]:

- время усреднения ПДД, t_n - период времени воздействия излучения с плотностью мощности S_n. Для «управляемого» воздействия время усреднения составляет 6 минут, тогда как для «неуправляемого» воздействия оно увеличивается до 30 минут. Затем любому источнику мощности разрешают вести передачу при больших уровнях мощности, чем предельные значения ПДД, пока средняя плотность мощности в (14) удовлетворяет предельному значению FCC для 30-минутного усреднения при «неуправляемом» воздействии.Where

- averaging time SDA, t _n - the time period of exposure to radiation with a power density S _n . For a “controlled” exposure, the averaging time is 6 minutes, while for an “uncontrolled" exposure it increases to 30 minutes. Then, any power source is allowed to transmit at higher power levels than the PDD limit values, while the average power density in (14) satisfies the FCC limit value for 30-minute averaging under "uncontrolled" exposure.

Based on this analysis, we determine adaptive power control methods to increase the current transmit power for each antenna, while at the same time maintaining the average power for each DIDO antenna below the traffic limits. We consider DIDO systems with more transmit antennas than active clients. This is a reasonable assumption, provided that DIDO antennas can be designed as inexpensive wireless devices (like WiFi access points) and installed wherever there is a DSL, cable modem, fiber optic, or other Internet connection.

The structure of DIDO systems with adaptive power control for each antenna is shown in FIG. 23. The amplitude of the digital signal coming from the multiplexer 234 is dynamically adjusted with scale power factors S ₁ , ..., S _M before being sent to the DAC units 235. Power scaling factors are calculated by the power control unit 232 based on the CQI 233.

. В одном из способов выполняют итерации по всем группам антенн согласно политике планирования кругового обслуживания, представленной на фиг. 24. В другом варианте осуществления различные способы планирования (т.е. пропорционально-справедливое планирование [8]) используют для выбора кластера для оптимизации частоты ошибок или характеристики пропускной способности.Each group contains at least as many DIDO antennas as there are active clients (K). At any given time, only one group has N _a > K active DIDO antennas transmitting to clients at a higher power level (S _o ) than the traffic limit limit

. In one method, iterates over all antenna groups according to the round-robin scheduling policy of FIG. 24. In another embodiment, various scheduling methods (ie, proportional fair scheduling [8]) are used to select a cluster to optimize the error rate or throughput characteristics.

Given the planning of power allocation according to the principle of circular service, from expression (14) we obtain the average transmit power for each DIDO antenna in the form:

. Коэффициент использования зависит от количества активных клиентов, количества групп и активных антенн на группу в соответствии со следующим определением:where t ₀ is the period of time during which the antenna group is active, and T _SDA = 30 min is the average time defined by the FCC guidelines [2]. The ratio in expression (15) is the utilization factor (DF) of the groups, defined so that the average transmit power from each DIDO antenna satisfies the maximum traffic value

. The utilization rate depends on the number of active clients, the number of groups and active antennas per group in accordance with the following definition:

The SNR gain (in dB) obtained in DIDO systems with power control and antenna grouping is expressed as a function of utilization:

We see that the amplifications in (17) are achieved due to the additional transmit power G _dB through all DIDO antennas.

Essentially, the total transmit power from all N _a from all groups N _g is defined as

where P _ij represents the average value of the transmit power to the antenna, defined by the expression:

and S _ij (t) is the power spectral density for the i-th transmit antenna within the j-th group. In one embodiment, the power spectral density in (19) is intended to optimize the error rate or throughput characteristics of each antenna.

To get some knowledge about the characteristics of the proposed method, consider 400 distributed DIDO antennas in a specific coverage area and 400 customers who have subscribed to the wireless Internet service offered through DIDO systems. It is unlikely that every Internet connection will be fully utilized all the time. Assume that 10% of customers will actively use a wireless Internet connection at any given time. The SNR gain as a result of such a transmission scheme is G _dB = 10log ₁₀ (1 / DF) = 10 dB, provided by an additional transmission power of 10 dB from all DIDO antennas. We see, however, that the average transmit power for each antenna is constant and is within the limit of the SDA.

In FIG. 25 is a comparison of the (non-encoded) SER characteristics of the aforementioned power control with antenna grouping and standard eigenmode selection in US 7,636,381. All schemes use BD precoding for four clients, each client is equipped with one antenna. SNR refers to the ratio of transmit power per antenna to noise power (i.e., SNR transmission per antenna). The curve, designated as DIDO 4 × 4, involves the use of four transmit antennas and precoding BD. The square curve indicates the SER characteristic with two additional transmit antennas and a BD with eigenmode selection, which results in a 10 dB SNR gain (at a target SER of 1%) compared to standard BD precoding. Power control with grouping of antennas and DF = 1/10 also leads to a gain of 10 dB at the same target value of SER. We see that the eigenmode selection changes the slope of the SER curve due to diversity gain, while our power control method shifts the SER curve to the left (maintaining the same slope) due to the increased average transmit power. For comparison, the SER value is shown with a high utilization coefficient DF = 1/50, which provides an additional gain of 7 dB compared to DF = 1/10.

It should be noted that our power control may have less complexity than standard methods of choosing our own mode. In fact, the antenna identifier in each group can be pre-computed and shared among DIDO antennas and clients through lookup tables, so that only K channel estimates are required at any given time. To select an eigenmode, (K + 2) channel estimates are calculated, and additional computational processing is required to select an eigenmode that minimizes SER at any time for all clients.

We then describe another method comprising grouping DIDO antennas to reduce the overhead of CSI feedback in some specific scenarios. In FIG. 26a presents one scenario in which clients (points) are scattered randomly in one area covered by multiple distributed DIDO antennas (crosses). The average power in each wireless transmission-reception communication line can be calculated as:

where H is a channel estimation matrix available in BTS DIDO.

Matrices A shown in FIG. 26a-c, are obtained in digital form by averaging channel matrices for 1000 cases. In FIG. 26b and 26c show two alternative scenarios, respectively, in which clients are grouped around a subset of DIDO antennas and receive negligible power from DIDO antennas located far away. For example, in FIG. 26b shows two groups of antennas forming a block diagonal matrix A. One extreme scenario is the case when each client is very close to only one transmitter and the transmitters are far apart, so that the power from all other DIDO antennas is negligible. In this case, the DIDO communication line breaks up into a plurality of SISO communication lines, and A is a diagonal matrix, as shown in FIG. 26s

In all three scenarios above, the BD precoding dynamically adjusts the precoding weights to account for different power levels on the wireless links between the DIDO antennas and the clients. However, it is sometimes convenient to identify multiple groups within a DIDO cluster and control DIDO precoding only within each group. Our proposed method of grouping leads to the following advantages.

- Winning in the calculations. DIDO precoding is calculated only within each group in the cluster. For example, if BD precoding is used, singular decomposition (SVD) has complexity O (n ₃ ), where n is the minimum dimension of the channel matrix H. If H can be reduced to a block diagonal matrix, SVD is calculated for each block with reduced complexity. In fact, if the channel matrix is divided into two block matrices with dimensions n ₁ and n ₂ such that n = n ₁ + n ₂ , the complexity of the SVD is only O (n ₁ ³ ) + O (n ₂ ³ ) <O (n ³ ) In the extreme case, if H is a diagonal matrix, the DIDO link splits into multiple SISO links and no SVD calculation is required.

- Reduced CSI feedback overhead. When DIDO antennas and clients are divided into groups, in one embodiment, CSIs are calculated from clients to antennas only within the same group. In TDD systems involving channel reversibility, antenna grouping reduces the number of channel estimates for calculating the channel matrix H. In FDD systems where CSI returns over a wireless link, antenna grouping provides an additional reduction in the overhead of CSI feedback over wireless links between DIDO antennas and by customers.

Multiple Access Technologies for DIDO Uplink

In one embodiment of the present invention, various multiple access technologies are defined for the uplink DIDO. These technologies can be used for CSI feedback or for transmitting data streams from clients to DIDO antennas on the uplink. In the following, we designate CSI feedback and data streams as upstream streams.

- Multiple inputs - multiple outputs (MIMO). Upstream streams are transmitted from the client to the DIDO antennas through open-loop MGMO multiplexing schemes. This method assumes that all clients are synchronized in time / frequency. In one embodiment, synchronization between clients is achieved by transmitting a training signal from the downlink, and all DIDO antennas are assumed to be synchronized with the same time / frequency reference clock. It should be noted that changes in delay propagation among different clients can generate a clock outage between the clocks of different clients, which can affect the characteristics of the uplink MIMO scheme. After clients have sent upstream streams through MIMO multiplexing schemes, DIDO receiving antennas can use non-linear (i.e., with a maximum probability, ML) or linear (i.e., with zero coefficients zeroing, minimum mean square error) receivers to suppress interference from adjacent channels and independent upstream demodulation.

- Time Division Multiple Access (TDMA). Different clients are assigned different time slots. Each client sends its upstream when its time slot is available.

- Frequency Division Multiple Access (FDMA). Different customers are assigned different carrier frequencies. In multi-carrier systems (OFDM), a subset of tonal frequencies is assigned to different clients simultaneously transmitting upstream streams, thereby reducing latency.

- Code Division Multiple Access (CDMA). Each client is assigned a different pseudo-random sequence, and orthogonality between the clients is achieved in the code domain.

In one embodiment of the present invention, clients are wireless devices transmitting at much lower power than DIDO antennas. In this case, the BTS DIDO determines client subgroups based on upstream SNR information to minimize subgroup interference. Within each subgroup, the aforementioned multiple access technologies are used to create orthogonal channels in the time, frequency, spatial or code domains, thereby avoiding uplink interference for different clients.

In another embodiment, the uplink multiple access techniques described above are used in conjunction with the antenna grouping techniques presented in the previous section to identify different client groups within a DIDO cluster.

System and method for adapting communication lines in multi-carrier DIDO systems

Methods of adapting communication lines for DIDO systems using the temporal, frequency, and spatial selectivity of wireless channels are defined in US Pat. No. 7,636,381. The following description provides embodiments of the present invention for adapting communication links in multi-carrier DIDO (OFDM) systems using time / frequency selectivity of wireless channels.

We simulate Rayleigh fading channels corresponding to an exponentially decaying power delay profile (PDP) or the Saleh-Valenzuela model in [9]. For simplicity, we consider a single-cluster channel with multi-path PDP, defined as:

- экспонента PDP, представляющая собой показатель когерентной полосы пропускания канала, обратно пропорциональный разбросу задержки канала (σ_DS). Низкие значения β дают в результате каналы с равномерной частотной характеристикой, тогда как высокие значения β создают частотно-избирательные каналы. PDP в (21) нормализуют так, что полная средняя мощность для всех ответвлений канала L унитарно равна:where n = 0, ..., L-1 is the channel branch index, L is the number of channel branches, and

- PDP exponent, which is an indicator of the coherent channel bandwidth, inversely proportional to the channel delay spread (σ _DS ). Low β values result in channels with a uniform frequency response, while high β values produce frequency selective channels. The PDPs in (21) are normalized so that the total average power for all branches of the channel L is unitarily equal to:

In FIG. 27 shows the amplitude in channels with low frequency selectivity (assuming β = 1) in the delay region or the current PDP (upper graph) and the frequency domain (lower graph) for 2 × 2 DIDO systems. The first subscript indicates the client, the second subscript indicates the transmitting antenna. Channels with high frequency selectivity (with β = 0.1) are shown in FIG. 28.

Next, we study the characteristics of DIDO precoding in frequency selective channels. We calculate the DIDO precoding weights by BD, taking the signal model in (1) satisfying the condition in (2). We reformulate the model of the received DIDO signal in (5) with the condition contained in (2) as:

- эффективная матрица канала для пользователя k. Для DIDO 2×2 с одной антенной на клиента эффективная канальная матрица уменьшается до одного значения с частотной характеристикой, представленной на фиг. 29, а для каналов, характеризующихся высокой частотной избирательностью (например, β=0.1), - на фиг. 28. Непрерывная линия на фиг. 29 обозначает клиента 1, а линия с точками обозначает клиента 2. На основе показателей качества канала на фиг. 29 мы определяем способы адаптации линий связи (LA) во временной/частотной области, динамически регулирующие MCS в зависимости от меняющихся режимов канала.Where

is the effective channel matrix for user k. For DIDO 2 × 2 with one antenna per client, the effective channel matrix is reduced to one value with the frequency response shown in FIG. 29, and for channels characterized by high frequency selectivity (for example, β = 0.1), in FIG. 28. The continuous line in FIG. 29 denotes client 1, and a dotted line denotes client 2. Based on the channel quality indicators in FIG. 29, we determine the adaptation of communication lines (LA) in the time / frequency domain, which dynamically regulate MCS depending on changing channel modes.

We begin by evaluating the performance of various MCSs in AWGN and SISO channels with Rayleigh fading. For simplicity, we assume that there is no FEC encoding, but the following LA methods can be extended to systems containing FEC.

In FIG. 30 shows an example SER for various QAM schemes (i.e. 4-QAM, 16-QAM, 64-QAM). Without loss of generality, we take the target value of SER equal to 1% for non-encoded systems. To meet this target SER value in AWGN channels, the threshold SNRs are 8 dB, 15.5 dB, and 22 dB for the three modulation schemes, respectively. For channels with Rayleigh fading, it is known that the SER characteristics for the above modulation schemes are worse than AWGN [13], and the threshold SNRs are: 18.6 dB, 27.3 dB, and 34.1 dB, respectively. We see that DIDO precoding converts the multi-user downlink into a series of parallel SISO communication lines. Therefore, the same SNR thresholds shown in FIG. 30 for SISO systems, stored for DIDO systems for each client on an individual basis. In addition, if the current LA is running, thresholds in the AWGN channels are used.

The key idea of the proposed LA method for DIDO systems is to use low-order MCS when the channel is subject to deep fading in the time or frequency domain (shown in FIG. 28) to ensure the reliability of the communication line. In contrast, when the channel is characterized by high gain, the LA method switches to higher order MCSs to increase spectral efficiency. One of the achievements of the present application compared to US Pat. No. 7,636,381 is the use of an effective channel matrix in (23) and in FIG. 29 as an indicator for permitting adaptation.

The general structure of the LA methods is shown in FIG. 31 and is defined as follows.

- CSI score. At step 3171, the BTS DIDO calculates the CSI from all users. Users can be equipped with a single or multiple receiving antennas.

- Precoded DIDO. At step 3172, the BTS calculates DIDO precoding weights for all users. In one embodiment, BD is used to calculate these weights. Pre-coding weights are calculated on a tone by tone basis.

- Calculation of the quality of the communication line. At step 3173, the BTS calculates link quality indicators in the frequency domain. In OFDM systems, metrics are calculated from the DIDO and CSI precoding weights for each tone. In one embodiment of the present invention, the link quality score is the average SNR of all OFDM tones. We define this method as LA1 (based on the average SNR characteristic). In another embodiment, the link quality indicator is the frequency response of the effective channel in (23). We define this method as LA2 (based on the characteristic for each tone to use frequency diversity). If each client has one antenna, the effective channel in the frequency domain is shown in FIG. 29. If customers have multiple receive antennas, the link quality score is defined as the Frobenius norm for the effective channel matrix for each tone. In an alternative embodiment, many communication link quality indicators are defined for each client as singular values of the effective channel matrix in (23).

- Bit loading algorithm. In step 3174, based on link quality metrics, the BTS determines the MCS for various clients and various OFDM tones. In method LA1, the same MCS is used for all clients and all OFDM tones based on the SNR thresholds for the Rayleigh fading channels, as shown in FIG. 30. In LA2, different MCSs are assigned to different OFDM tones to use frequency diversity channels.

- Transmission of pre-encoded data. At step 3175, the BTS transmits the precoded data streams from the distributed DIDO antennas to clients using MCS obtained from the bit-loading algorithm. A header is added to the precoded data to inform clients of different MCSs for different tones. For example, if eight MCSs are available and OFDM symbols are determined using N = 64 tones, log ₂ (8) * N = 192 bits are required in order to report the current MCS to each client. Assuming that a 4-QAM scheme (spectral efficiency of 2 bits / symbol) is used to display these bits in symbols, a total of 192/2 / N = 1.5 OFDM symbols is needed to display MCS information. In another embodiment, multiple subcarriers (or OFDM tones) are grouped into subbands, and the same MCS is assigned to all tones in the same subband to reduce overhead due to control information. In addition, MCSs are adjusted based on temporal changes in channel gain (proportional to coherence time). In a fixed wireless channel (characterized by a low Doppler effect), MCSs are recalculated at each part of the channel coherence time, thereby reducing the overhead required for control information.

In FIG. 32 is a SER characteristic of the LA methods described above. For comparison, the SER characteristic in channels with Rayleigh fading is graphically depicted for each of the three QAM schemes used. Method LA2 adapts the MCS to frequency channel oscillations in the frequency domain, thus providing a gain of 1.8 bit / s / Hz in spectral efficiency for a low SNR (i.e., SNR = 20 dB) and SNR gain of 15 dB ( for SNR> 35 dB) compared to LA1.

System and method for interpolating DIDO precoding in multi-carrier systems

The computational complexity of DIDO systems is localized mainly in a centralized processor or BTS. The most computationally expensive operation is to calculate the precoding weights for all clients by their CSI. When using precoding BD, the BTS must perform as many singular decomposition (SVD) operations as there are clients in the system. One way to reduce complexity is through parallel processing, where SVDs are computed on a separate processor for each client.

In multi-carrier DIDO systems, each subcarrier uses a shallow fading channel, and SVDs are performed for each client on each subcarrier. Clearly, system complexity increases linearly with the number of subcarriers. For example, in OFDM systems with a 1 MHz signal bandwidth, a cyclic prefix (L ₀ ) must have at least eight channel branches (ie, a duration of 8 microseconds) in order to avoid intersymbol interference in urban streets with macro cells with a large delay spread [ 3]. The Fast Fourier Transform (FFT) size (N _FFT ) used to generate OFDM symbols is typically set to a multiple of L ₀ to reduce data rate loss. If N _FFT = 64, then the effective spectral efficiency of the system is limited by the coefficient N _FFT / (N _FFT + L ₀ ) = 89%. Larger N _FFT values result in higher spectral efficiency due to higher computational complexity in the DIDO precoder.

One way to reduce computational complexity in a DIDO precoder is to perform an SVD operation for a subset of tones (called experimental tones) and obtain precoding weights for the remaining tones by interpolation. Interpolation of scales is one of the sources of error leading to inter-client interference. In one embodiment, optimal weight interpolation methods are used to reduce inter-client interference, which improves the error rate and reduces computational complexity in multi-carrier systems. In DIDO systems with M transmit antennas, U clients, and N receive antennas per client, the condition for the k-th client precoding weights (W _k ), guaranteeing zero interference to other clients, is obtained from (2) as follows:

where H _u are the channel matrices corresponding to other DIDO clients in the system.

In one embodiment of the present invention, the objective function of the method of interpolating weights is defined as:

- матрица интерполяции веса и

- норма Фробениуса для матрицы. Проблему оптимизации формулируют как:where θ _k is a set of parameters that will be optimized for user k,

- weight interpolation matrix and

is the Frobenius norm for the matrix. The optimization problem is formulated as:

where Θ _k is the feasible set of the optimization problem, and θ _{k, opt} is the optimal solution.

The objective function in (25) is determined for one OFDM tone. In another embodiment of the present invention, the objective function is defined as a linear combination of the Frobenius norm in (25) matrices for all OFDM tones to be interpolated. In another embodiment, the OFDM spectrum is divided into subsets of tones, and the optimal solution is:

where n is the OFDM tone index, and A is a subset of tones.

The matrix W _k (θ _k ) of the interpolation of weights in (25) is expressed as a function of the set θ _{k of} parameters. When the optimal set is determined according to (26) or (27), the optimal weight matrix is calculated. In one embodiment of the present invention, a weight interpolation matrix for a given OFDM tone n is defined as a linear combination of experimental tone weight matrices. One example of the function of interpolating weights for beamforming systems with a single client was defined in [11]. In DIDO multi-client systems, we write the weight interpolation matrix as:

для обеспечения унитарной передачи мощности от каждой антенны. Если N=1 (одна приемная антенна на клиента), матрица в (28) становится вектором, нормализованным относительно его нормы. В одном варианте осуществления настоящего изобретения экспериментальные тональные сигналы выбирают единообразно в пределах диапазона тональных сигналов OFDM. В другом варианте осуществления экспериментальные тональные сигналы выбирают адаптивно, основываясь на CSI, для минимизации ошибки интерполяции.where 0≤l≤ (L ₀ -1), L ₀ is the number of experimental tones and c _n = (n-1) / N ₀ when N ₀ = N _FFT / L ₀ . The weight matrix in (28) is then normalized so that

to provide unitary power transfer from each antenna. If N = 1 (one receiving antenna per client), the matrix in (28) becomes a vector normalized with respect to its norm. In one embodiment of the present invention, the experimental tones are selected uniformly within the range of OFDM tones. In another embodiment, experimental tones are selected adaptively based on CSI to minimize interpolation errors.

We see that one of the main differences between the system and method in [11] from those proposed in this patent application is an objective function. In particular, the systems in [11] assume many transmitting antennas and one client, so the corresponding method is developed with the possibility of maximizing the product of precoding weights per channel to maximize the reception SNR for the client. This method, however, does not work in multi-client scenarios, since it leads to inter-client interference due to an interpolation error. In contrast, our method is designed to minimize inter-client interference and thus improve the error rate for all customers.

In FIG. 33 shows the matrix elements in (28) depending on the OFDM tone index for a 2 × 2 DIDO system with N _FFT = 64 and L ₀ = 8. The channel PDP is generated according to the model in (21) with β = 1, and the channel consists of only eight branches. We see that L ₀ must exceed the number of channel branches. The solid lines in FIG. 33 represent ideal functions, while dashed lines represent interpolated functions. The interpolated weights coincide with those ideal for experimental tonal signals according to the definition given in (28). The weights calculated for the remaining tones only come close to the ideal case due to estimation error.

One of the ways to implement the method of interpolating weights is an exhaustive search on a feasible set Θ _k in (26). To reduce the complexity of the search, we quantize the feasible set by the values of P uniformly in the range [0.2π]. In FIG. 34 shows the dependence of SER on SNR for L ₀ = 8, M = Nt = 2 transmitting antennas and a variable number P. As the number of quantization levels increases, the SER characteristic improves. We observe a case where P = 10 approaches the indicator P = 100 with much less computational complexity due to a reduction in the number of searches.

In FIG. 35 shows the SER characteristic of the interpolation method for various orders of DIDO and L ₀ = 16. We assume that the number of clients is equal to the number of transmitting antennas and each client is equipped with one antenna. As the number of customers increases, the SER characteristic deteriorates due to an increase in inter-client interference caused by the interpolation error of the balance.

In another embodiment of the present invention, interpolation functions of weights other than those presented in (28) are used. For example, autoregressive linear prediction models [12] can be used to interpolate weights using various OFDM tones based on channel frequency correlation estimates.

Literature

[1] A. Forenza and S.G. Perlman, “System and method for distributed antenna wireless communications,” US Patent Application No. 12 / 630,627, filed December 2, 2009, “System and Method for Wireless Communication with Distributed Antennas.”

[2] FCC, “Evaluating compliance with FCC guidelines for human exposure to radiofrequency electromagnetic fields”, OET Bulletin 65, ed. 97-01, August 1997

[3] 3GPP, “Spatial Channel Model AHG (Combined ad-hoc from 3GPP & 3GPP2)”, SCM Text, version 6.0, April 22, 2003

[4] 3GPP TR 25.912, “Feasibility Study for Evolved UTRA and UTRAN”, version 9.0.0 (October 2009).

[5] 3GPP TR 25.913, “Requirements for Evolved UTRA (E-UTRA) and Evolved UTRAN (E-UTRAN)”, version 8.0.0 (January 2009).

[6] W.S. Jakes, Microwave Mobile Communications, IEEE Press, 1974

[7] K.K. Wong, et al., “A joint channel diagonalization for multiuser MIMO antenna systems”, IEEE Trans. Wireless Comm., Volume 2, pp. 773-786, July 2003.

[8] P. Viswanath, et al., “Opportunistic beamforming using dump antennas”, IEEE Trans. On Inform. Theory, Volume 48, pp. 1277-1294, June 2002

[9] A.A.M. Saleh, et al., “A statistical model for indoor multipath propagation,” IEEE Jour. Select Areas in Comm., Volume 195 SAC-5, No. 2, pp. 128-137, February 1987

[10] A. Paulraj, et al., Introduction to Space-Time Wireless Communications, Cambridge University Press, 40 West 20th Street, New York, NY, USA, 2003.

[11] J. Choi, et al., “Interpolation Based Transmit Beamforming for MIMO-OFDM with Limited Feedback”, IEEE Trans, on Signal Processing, Volume 53, No. 11, pp. 4125-4135, November 2005.

[12] I. Wong, et al., “Long Range Channel Prediction for Adaptive OFDM Systems,” Proc. of the IEEE Asilomar Conf. on Signals, Systems, and Computers, Volume 1, pp. 723-736, Pacific Grove, California, USA, November 2004.

[13] J.G. Proakis, Communication System Engineering, Prentice Hall, 1994

[14] B.D. Van Veen, et al., “Beamforming: a versatile approach to spatial filtering”, IEEE ASSP Magazine, April 1988.

[15] R.G. Vaughan, “On optimum combining at the mobile,” IEEE Trans. On Vehic. Tech., Volume 37, No. 4, pp. 181-188, November 1988.

[16] F. Qian, “Partially adaptive beamforming for correlated interference rejection”, IEEE Trans. On Sign. Proc., Volume 43, No. 2, pp. 506-515, February 1995.

[17] H. Krim, et. al., “Two decades of array signal processing research”, IEEE Signal Proc. Magazine, pp. 67-94, July 1996.

[19] W.R. Remley, "Digital beamforming system", US patent 4,003,016, January 1977

[18] R.J. Masak, “Beamforming / null-steering adaptive array”, US Pat. No. 4,771,289, September 1988.

[20] K.-B. Yu, et. al., "Adaptive digital beamforming architecture and algorithm for nulling mainlobe and multiple sidelobe radar jammers while preserving monopulse ratio angle estimation accuracy", US patent 5,600,326, February 1997.

[21] H. Boche, et al., “Analysis of different precoding / decoding strategies for multiuser beamforming”, IEEE Vehic. Tech. Conf., Volume 1, April 2003

[22] M. Schubert, et al., “Joint Dirty Rareg Pre-coding and downlink beamforming,” Volume 2, pp. 536-540, December 2002.

[23] H. Boche, et al. “A general duality theory for uplink and downlink beamformingc, Volume 1, pp. 87-91, December 2002.

[24] K.K. Wong, R.D. Murch, and K.B. Letaief, “A joint channel diagonalization for multiuser MIMO antenna systems”, IEEE Trans. Wireless Comm., Volume 2, pp. 773-786, July 2003.

[25] Q. N. Spencer, A.L. Swindlehurst, and M. Haardt, “Zero forcing methods for downlink spatial multiplexing in multiuser MIMO channels”, IEEE Trans. Sig. Proc., Volume 52, pp. 461-471, February 2004.

II. Disclosure from related application No. 12 / 917,257

The following describes wireless radio frequency (RF) communication systems and methods comprising a plurality of distributed transmit antennas working together to create wireless links to predetermined users, while suppressing interference to other users. Coordination through various transmit antennas is enabled through user clustering. A user cluster is a subset of transmitting antennas whose signal a given user can reliably detect (i.e., the received signal power is higher than noise or interference). Each user in the system forms his own user cluster. The signals sent by transmitting antennas within the same user cluster are coherently combined to create RF energy at the location of the target user and a point with zero RF interference at the location of any other user accessible to these antennas.

Consider a system with M transmit antennas within the same user cluster and K users available for these M antennas at K≤M. Suppose the transmitters receive CSI (H∈C ^{K × M} ) between M transmit antennas and K users. For simplicity, we assume that each user is provided with one antenna, but this method can be extended to many receiving antennas per user. Consider the channel matrix H obtained by combining the vectors (h _k ∈ C ^{1 × M} ) of channels from M transmitting antennas to K users, in the form:

The precoding weights (w _k ∈C ^{M × 1} ) that create the RF energy for user k and reduce the RF energy to zero for all other K-1 users are calculated to satisfy the following condition:

- эффективная канальная матрица пользователя k, полученная удалением k-й строки матрицы Н, а 0^K×1 - вектор со всеми нулевыми элементами.Where

is the effective channel matrix of user k obtained by deleting the kth row of the matrix H, and 0 ^{K × 1} is a vector with all zero elements.

In one embodiment, the wireless system is a DIDO system, and user clustering is used to create a wireless link to a target user, previously suppressing interference to any other user accessible by antennas lying within the user cluster. US Patent Application No. 12 / 630,627 describes a DIDO system including:

- DIDO clients: user terminals equipped with one or multiple antennas.

- DIDO Distributed Antennas: transceiver stations working together to transmit precoded data streams to multiple users, thus suppressing inter-user interference.

- Base Transceiver Stations (BTS) DIDO: A centralized processor that generates precoded signals for distributed DIDO antennas.

- Base Station Network (BSN) DIDO: A wired transport communications network connecting the BTS to the distributed DIDO antennas or other BTS.

Distributed DIDO antennas are grouped into various subsets depending on their spatial distribution relative to the location of the DIDO or BTS clients. We define three types of clusters, as shown in FIG. 36.

- Super cluster 3640: a plurality of distributed DIDO antennas connected to one or a plurality of BTSs, so that the two-way delay between all BTSs and their respective users is within the precoding cycle limit for DIDO.

- DIDO 3641 Cluster: multiple distributed DIDO antennas connected to the same BTS. When a supercluster contains only one BTS, its definition matches the DIDO cluster.

- User cluster 3642: a plurality of distributed DIDO antennas that jointly transmit precoded data to a particular user.

For example, BTSs are local hubs connected to other BTSs and distributed DIDO antennas via a BSN. The BSN may include various network technologies, including without limitation digital subscriber lines (DSL), ADSL, VDSL [6], cable modems, fiber optic rings, T1 lines, hybrid fiber optic coaxial (HFC) networks and / or fixed wireless (for example, WiFi ) All BTS within the same supercluster share DIDO precoding information through the BSN, so that the two-way delay is within the DIDO precoding cycle.

In FIG. 37 dots indicate distributed DIDO antennas, crosses indicate users, and dashed lines indicate user clusters for users U1 and U8, respectively. The method described below is intended to create a communication line to the target user U1, creating points of zero RF energy for any other user (U2-U8) inside or outside the user cluster.

We proposed a similar method in [5], where points of zero RF energy were created to remove interference in the overlapping regions between DIDO clusters. Additional antennas were needed to transmit the signal to clients within the DIDO cluster while suppressing intercluster interference. One embodiment of the method proposed in this application does not involve the removal of intercluster interference for DIDO clusters; rather, it is assumed that the cluster is connected to the client (i.e., the user cluster) and ensures that no interference (or minor interference) is generated by any other client in its environment.

One idea associated with the proposed method is that users far enough away from the user cluster are not affected by radiation from transmitting antennas due to large losses in the signal path. Users located near or within the user cluster receive a non-interfering signal due to precoding. In addition, additional transmit antennas can be added to the user cluster (as shown in FIG. 37), so that the condition K≤M is met.

One embodiment of a method using user clustering consists of the following steps.

but. Link Quality Measurement: Link quality between each distributed DIDO antenna and each user is transmitted to the BTS. The link quality index is a signal-to-noise ratio (SNR) or a signal-to-noise interference-to-noise ratio (SINR).

In one embodiment, the distributed DIDO antennas transmit training signals, and users evaluate the quality of the received signal based on these training signals. The training signals are designed to be orthogonal in the time, frequency or code domain so that users can distinguish between different transmitters. In an alternate embodiment, DIDO antennas transmit narrowband signals (i.e., a single tone) at one specific frequency (i.e., a beacon channel), and users evaluate link quality based on that beacon signal. One threshold value is defined as the minimum signal amplitude (or power) above the noise level for successful data demodulation, as shown in FIG. 38a. Any indicator of the quality of the communication line below this threshold value is taken equal to zero. The quality index of the communication line is quantized by a finite number of bits and returned to the transmitter.

In another embodiment, training signals or beacons are transmitted from users, and the quality of the communication line is evaluated on DIDO transmit antennas (as in FIG. 38b), taking into account the reversibility of the path loss between the uplink (UL) and downlink (DL). It should be noted that the reversibility of the path loss is a realistic assumption in time division duplex (TDD) systems (with UL and DL channels at the same frequency) and frequency division duplex systems (FDD) when the UL frequency bands and DL are relatively close.

Communication link performance metrics are shared between different BTSs via the BSN, as shown in FIG. 37, so that all BTS have link quality information between each antenna / user pair in different DIDO clusters.

b. Definition of user clusters: link quality indicators of all wireless links in DIDO clusters are elements in the link quality matrix shared by all BTS via BSN. One example of a link quality matrix for the scenario of FIG. 37 is shown in FIG. 39.

The link quality matrix is used to determine user clusters. For example, in FIG. 39 illustrates user cluster selection for user U8. First, a subset of transmitters with non-zero link quality indicators (i.e., active transmitters) is identified for user U8. These transmitters fill the user cluster for user U8. Then, a submatrix is selected containing nonzero elements introduced by transmitters within the user cluster to other users. It should be noted that, since the quality indicators of communication lines are used only to select a user cluster, they can be quantized with only two bits (i.e., to identify the state above or below the threshold values in Fig. 38), thus reducing the overhead of the reverse communication.

Another example is shown in FIG. 40 for user U1. In this case, the number of active transmitters is lower than the number of users in the submatrix, which violates the condition K≤M. Therefore, one or more columns are added to the submatrix to fulfill this condition. If the number of transmitters exceeds the number of users, additional antennas can be used for diversity schemes (i.e., either an antenna or a choice of its own mode).

Another example is shown in FIG. 41 for user U4. We see that the sub-matrix can be obtained as a combination of two sub-matrices.

c. CSI message on the BTS. After selecting user CSI clusters from all transmitters within the user cluster for each user reached by these transmitters, it will be available for all BTS. CSI information is shared by all BTS via BSN. In TDD systems, the reversibility of the UL and DL channels can be used to obtain CSI when the training signal passes through the UL channel. FDD systems require feedback channels from all users to the BTS. To reduce the amount of feedback, only the CSI corresponding to nonzero elements of the link quality matrix is sent as feedback.

d. DIDO precoding. Finally, DIDO precoding is applied to each CSI submatrix corresponding to different user clusters (as described, for example, in related US patent applications).

, а вес w_k предварительного кодирования для пользователя k определяют как правый сингулярный вектор, соответствующий нулевому подпространству

. В альтернативном варианте осуществления, если М>K и SVD разлагает эффективную канальную матрицу как

, предварительно кодированный вес DIDO для пользователя k определяет выражение:In one embodiment, the singular decomposition (SVD) of the effective channel matrix is calculated

and the precoding weight w _k for user k is defined as the right singular vector corresponding to the zero subspace

. In an alternative embodiment, if M> K and SVD decomposes the effective channel matrix as

, the precoded DIDO weight for user k defines the expression:

.where U _o is the matrix whose columns are singular vectors of the zero subspace

.

равен собственному вектору С, соответствующему нулевому собственному значению:Based on the main considerations of linear algebra, we see that the right singular vector in the zero subspace of the matrix

equal to the eigenvector C corresponding to the zero eigenvalue:

, согласно SVD. Далее, альтернативу вычислениям SVD для

представляет собой вычисление разложения собственного значения С. Есть несколько способов вычисления разложения собственного значения, например способ мощности. Поскольку нас интересует только собственный вектор, соответствующий нулевому подпространству С, мы используем обратный степенной метод, описанный итерацией:where the effective channel matrix is decomposed as

according to SVD. Next, an alternative to SVD calculations for

is a calculation of the decomposition of the eigenvalue of C. There are several ways to calculate the decomposition of the eigenvalue, for example, the power method. Since we are only interested in the eigenvector corresponding to the zero subspace C, we use the inverse power method described by the iteration:

where the vector (u _i ) at the first iteration is a random vector.

Considering that the eigenvalue (λ) of the zero subspace is known (i.e., zero), the inverse power method requires that only one iteration converges, which reduces computational complexity. Then we write the precoding weight vector as:

where u ₁ is a vector with real elements equal to 1 (i.e., the precoding weight vector is the sum of C ^-1 columns).

DIDO precoding calculation requires a single matrix inversion. There are several numerical solutions to reduce the complexity of matrix inversions, such as the Strassen algorithm [1] or the Coppersmith-Winograd algorithm [2, 3]. Since C is a Hermitian matrix by definition, an alternative solution is to decompose C into real and imaginary components and calculate the matrix inversion of the real matrix according to the method described in [4, Section 11.4].

Another distinctive feature of the proposed method and system is their reconfigurability. As the client moves through the various DIDO clusters, as shown in FIG. 42, the user cluster follows its movements. In other words, a subset of the transmit antennas is constantly updated as the client changes its position and the effective channel matrix (and the corresponding precoding weights) are re-computed.

The method proposed in the present description operates within the supercluster shown in FIG. 36, since the links between the BTS via the BSN must have a low delay. To suppress interference in the overlapping region of various superclusters, we can apply our method described in [5], using additional antennas to create points of zero RF energy in the interfering regions between DIDO clusters.

It should be noted that the terms “user” and “client” are used interchangeably herein.

Literature

[1] S. Robinson, “Toward an Optimal Algorithm for Matrix Multiplication,” SIAM News, Volume 38, No. 9, November 2005.

[2] D. Coppersmith and S. Winograd, “Matrix Multiplication via Arithmetic Progression”, J. Symb. Sotr., Volume 9, pp. 251-280, 1990.

[3] N. Cohn, R. Kleinberg, W. Szegedy, S. Umans, “Group-theoretic Algorithms for Matrix Multiplication,” pp. 379-388, November 2005.

[4] W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flarmery, “NUMEPJCAL RECIPES IN C: THE ART OF SCIENTIFIC COMPUTING,” Cambridge University Press, 1992.

[5] A. Forenza and S.G. Perlman, “Interference management, handover strategies, power management and link adaptation in distributed input-distributed output (DIDO) communication systems”, patent application filed No. 12 / 802,988, filed June 16, 2010

[6] Per-Erik Eriksson and Bjorn Odenhammar, “VDSL2: Next important broadband techno logy”, Ericsson Review No. 1, 2006

III. Systems and methods for using coherence areas in wireless systems

Multi-Antenna System (MAS) throughput under real-world propagation conditions is a function of the spatial diversity available on a wireless link. Spatial diversity is determined through the distribution of scattering objects in the wireless channel, as well as the geometry of the transmitting and receiving antenna arrays.

One of the popular MAS models is the so-called cluster channel model, which defines groups of scatterers as clusters located around transmitters and receivers. In general, the more clusters and the greater their angular divergence, the higher the spatial diversity and throughput achievable for wireless communication lines. Cluster channel models are recognized to be effective based on many actual measurements [1-2], and variations of such models have been adopted by various standards for indoor wireless connections (for example, IEEE 802.11n Technical Group [3] for WLAN) and outdoor (3GPP Technical Specification Group for 3G cellular systems [4]).

Other factors that determine spatial diversity in wireless channels are the parameters of the antenna arrays, including: the distance between the antenna elements [5-7], the number of antennas [8-9], the aperture of the array [10-11], the geometry of the array [5, 12 , 13], polarization and antenna pattern [14-28].

A single model describing the effect of the antenna array device and the parameters of the propagation channel on the spatial diversity (or degree of freedom) of a wireless communication line is presented in [29]. The model of the received signal in [29] is described by the equation:

- матрица, описывающая отклик системы между элементами передающего и приемного вектора и имеющая вид:where x (p) ∈C ³ is the polarized vector describing the transmitted signal, p, q∈R ³ are the elements of the polarized vector describing the transmitting and receiving antenna arrays, respectively, and

- a matrix describing the response of the system between the elements of the transmitting and receiving vector and having the form:

- характеристики передающей и приемной антенных решеток соответственно, а

- матрица отклика канала, где элементы матрицы представляют собой комплексное усиление между направлением передачи

и направлением приема

. В системах DIDO устройства пользователя могут иметь одну или множество антенн. Для упрощения примем, что мы имеем дело с приемником с единственной антенной и идеальной изотропной диаграммой. Приведем матрицу отклика системы к следующему виду:Where

- characteristics of the transmitting and receiving antenna arrays, respectively, and

- channel response matrix, where the matrix elements are the complex gain between the direction of transmission

and direction of admission

. In DIDO systems, user devices may have one or multiple antennas. To simplify, we assume that we are dealing with a receiver with a single antenna and an ideal isotropic diagram. We bring the response matrix of the system to the following form:

.where only the transmit antenna diagram is taken into account

.

Based on the Maxwell equations and the limitation of the far zone of the Green's function, the lattice response can be approximately expressed as [29]:

when p∈P, and P is the space that defines the antenna array, and where:

. Для неполяризованных антенн изучение отклика решетки равноценно изучению ядра интеграла, представленного выше. Далее мы показываем конечный вид выражения для ядер интеграла для различных типов решеток.at

. For unpolarized antennas, studying the lattice response is equivalent to studying the core of the integral presented above. Next, we show the final form of the expression for the kernels of the integral for various types of lattices.

Non-polarized linear antenna arrays

For unpolarized linear arrays of length L (normalized by wavelength) and antenna elements oriented along the z axis and centered at the origin, the kernel of the integral has the form [29]:

a (cosθ, p _z ) = exp (-j2πp _z cosθ).

Expanding this expression to a series of shifted dyads, we obtain that the function sinc has a resolution of 1 / L, and the dimension of the subspace bounded by the lattice and approximately bounded by the wave vector (i.e., the degree of freedom) has the form:

. Мы рассматриваем это для поперечно-направленных антенных решеток

, поскольку для продольно-направленных антенн

.Where

. We consider this for cross-directional antenna arrays.

since for longitudinally directed antennas

.

Non-polarized spherical antenna arrays

The core of the integral for a spherical lattice of radius R (normalized by the wavelength) has the form [29]:

Having decomposed the above function into the sum of Bessel spherical functions of the first kind, we obtain the resolution of the spherical lattice in the form 1 / (πR ² ), and the number of degrees of freedom in the form:

.where A is the area of the spherical lattice and

.

Coherence Areas in Wireless Channels

The relationship between the resolution of spherical antenna arrays and their area A is shown in FIG. 43. The sphere in the center is a spherical lattice with area A. The projection of the channel clusters onto a single sphere defines various scattering regions with dimensions proportional to the angular divergence of the clusters. A region of size 1 / A inside each cluster, called a “region of coherence," denotes the projection of the basis functions of the radiation field of the grating and determines the resolution of the grating in the space of wave vectors.

Comparing FIG. 43 and FIG. 44, we observe that the size of the coherence region decreases inversely with the size of the lattice. In fact, large gratings can focus energy on smaller areas, which ultimately gives more degrees of freedom DF. It should be noted that the total number of degrees of freedom also depends on the angular divergence of the cluster, as indicated in the definition above.

In FIG. 45 shows another example in which the size of the grating covers an even larger area than in FIG. 44, which leads to the appearance of additional degrees of freedom. In DIDO systems, the lattice aperture can be approximately calculated from the total area covered by all DIDO transmitters (assuming that the antennas are spaced apart by a wavelength distance). Further, in FIG. 45 shows that in DIDO systems it is possible to increase the number of degrees of freedom by distributing the antennas in space, which reduces the size of the coherence regions. It should be noted that all the figures are generated under the assumption that the lattices have an ideal spherical shape. In practice, DIDO antennas are randomly placed over fairly large areas, and the resulting shape of the coherence regions may not have such an ideal shape as shown in the figures.

In FIG. 46 shows that as the size of the grating increases, wireless channels include more clusters, since more objects located between DIDO transmitters scatter radio waves. As a result of this, it is possible to obtain a larger number of basis functions (covering the radiation field), which will lead to the appearance of additional degrees of freedom in accordance with the above definition.

The multi-user (MU) multi-antenna systems (MAS) described in this patent application use the coherence domain of a wireless channel to create multiple simultaneous, independent, non-interfering data streams for different users. For given channel conditions and user distribution, the basic functions of the radiation field of the grating are selected to create independent simultaneous wireless communication lines for different users in such a way that each user does not encounter interference in the communication line. When the channels between each transmitter and each user are known to the MU-MAS, the precoding transmission is adjusted based on this information to create separate areas of coherence for different users.

In one embodiment of the present invention, the MU-MAS is configured to use non-linear pre-coding, for example, dirty-paper coding (DPC) [30-31] or Tomlinson-Harashima (T) precoding [32-33]. In another embodiment of the present invention, the MU-MAS is configured to use non-linear precoding, such as block diagonalization (BD) precoding, as described in our previous patent applications [0003-0009], or beamforming with a controlled zero position (ZF -BF) [34].

To start MU-MAS precoding, you must have Channel Status Information (CSI). CSI is available to MU-MAS through feedback or uplink channel assumption, assuming uplink / downlink reversibility is possible in time division duplex (TDD) systems. One way to limit the amount of feedback required by CSI is to use limited feedback methods [35-37]. In one embodiment, the MU-MAS is configured to use limited feedback techniques to reduce CSI overhead on the control channel. Designing a codebook is very important for limited feedback methods. In one embodiment, the encoding book is created from basic functions covering the radiation field of the transmitting array.

If users move in space or the data medium changes over time due to moving objects (such as people or cars), the coherence regions change their location and shape. This is due to the well-known Doppler effect in wireless communications. The MU-MAS described in this patent application regulates precoding to constantly adapt the coherence region for each user when the environment changes due to the Doppler effect. Such adaptation of the coherence regions creates simultaneous channels that do not interfere with each other for different users.

In another embodiment of the present invention, adaptive selection of a subset of antennas of the MU-MAS system is used to create coherence regions of various sizes. For example, if the location of the subscribers is greatly spaced in space (for example, in the suburbs, or at a time of the day that uses little wireless resources), only a small subset of antennas is selected, and the size of the coherence region strongly depends on the size of the array, as shown in FIG. 43. In an alternative embodiment, in heavily populated areas (for example, in urban areas, or at such times of the day that peak use of wireless services), more antennas are chosen to create small areas of coherence for users in close proximity to each other.

In one embodiment of the present invention, the MU-MAS is a DIDO system as described in previous patent applications [0003-0009]. In DIDO systems, linear or non-linear precoding and / or limited feedback methods are used to create a coherence region for different users.

Numerical results

Let's start by calculating the number of degrees of freedom in conventional systems with multiple inputs - multiple outputs (MIMO) depending on the size of the grating. Consider non-polarized linear antenna arrays and two types of channel models: those located indoors, as in the standard for WiFi IEEE 802.11n, and outdoors, as in the standard for 3GPP-LTE cellular communication systems. The indoor channel model in [3] determines the number of clusters in the range [2, 6] and the angular divergence in the range [15 °, 40 °]. An outdoor channel model in a small urban area defines about 6 clusters and an angular divergence of the base station of approximately 20 °.

In FIG. 47 shows the degrees of freedom of MIMO systems in real-world indoor and outdoor distribution scenarios. For example, in the case of linear arrays with ten antennas located at a wavelength distance from each other, the maximum number of degrees of freedom (or the number of spatial channels) available for a wireless communication line is limited to about three for outdoor scenarios and an indoor family. Of course, indoor channels provide more degrees of freedom due to greater angular divergence.

Next, we calculate the degrees of freedom in DIDO systems. Consider the case in which the antennas are located in three-dimensional space, for example, in a scenario for the city center, where DIDO access points can be located on different floors of neighboring buildings. In this case, in the model, we consider the spherical arrays of the DIDO transmit antennas (interconnected by means of fiber optic or DSL backbone network). We also assume that the clusters are evenly spaced at a spatial angle.

In FIG. 48 shows the degrees of freedom in DIDO systems depending on the diameter of the grating. We see that for a diameter corresponding to ten wavelengths, approximately 1000 degrees of freedom are available in DIDO systems. In theory, users can create 1000 channels that do not interfere with each other. The increase in spatial diversity due to space-distributed antennas is a key point in increasing the gain in DIDO systems compared to MIMO systems.

. Например, для сценария с двухэтажными зданиями в пригородной зоне вертикальный угол рассеивателей может составлять α=60°. Количество степеней свободы для данного случая в зависимости от длины волны представлено на фиг. 48.For comparison, we show the degrees of freedom attainable in a suburban environment with DIDO systems. We assume that the clusters are spaced within the vertical angles [α, π-α], and we determine the spatial angle of the clusters as

. For example, for a scenario with two-story buildings in a suburban area, the vertical angle of the diffusers may be α = 60 °. The number of degrees of freedom for this case depending on the wavelength is shown in FIG. 48.

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IV. Systems and methods for the planned development and obsolescence of the multi-user spectrum

The increasing demand for high-speed wireless services and the growing number of cellular subscribers have caused a radical technical revolution in the wireless technology industry over the past three decades, from the original voice services (AMPS [1-2]) to standards that support digital speech (GSM [3-4] , IS-95 CDMA [5]), data stream (EDGE [6], EV-DO [7]) and Internet browsing (WiFi [8-9], WiMAX [10-11], 3G [12-13 ], 4G [14-15]). The growth of wireless technology in recent years has been achieved by two important achievements.

i) The US Federal Communications Commission (FCC) [16] has identified a new spectrum to support new standards. For example, in first-generation AMPS systems, the number of channels distributed by the FCC grew from the original 333 in 1983 to 416 in the late 1980s to support the growing number of cellular subscribers. In recent years, the commercialization of technologies such as Wi-Fi, Bluetooth, and ZigBee has become possible thanks to the use of the unlicensed ISM band allocated by the FCC in 1985 [17].

ii) The wireless technology industry is creating new ways that make better use of the limited available spectrum to support communication lines with higher data rates and an increased number of clients. A significant revolution in the world of wireless technologies was the transition from analog AMPS to digital D-AMPS and GSM in the 1990s, which made it possible to increase the volume of calls on a given frequency band due to increased spectral efficiency. Another radical change was the creation of spatial processing technologies in the early 2000s, such as technology with multiple inputs - multiple outputs (MIMO), which led to a fourfold increase in data transfer speed compared to previous wireless networks, as well as their use in various standards (e.g. IEEE 802.11n for Wi-Fi, IEEE 802.16 for WiMAX, 3GPP for 4G-LTE).

Despite attempts to create high-speed wireless connectivity solutions, the wireless technology industry has faced new challenges: the need to stream high-quality (HD) video to meet the growing needs of services such as the gaming industry, as well as provide wireless coverage everywhere (including rural areas, where cabling is very expensive and impractical). Currently, the most advanced wireless standards systems (for example, 4G-LTE) cannot meet the data transfer speed requirements and meet the delay time limits for high-quality streaming services, especially where the network is overloaded with a large volume of simultaneously operating communication lines . And again, the main disadvantages are the limited availability of the spectrum and the lack of spectrally efficient technologies that can truly increase the data transfer rate and provide full coverage.

In recent years, a new technology has been developed called technology with distributed input - distributed output (DIDO) [18-21] and described in our previous patent applications [0002-0009]. DIDO technology promises to increase the effective use of the spectrum several times, which will allow services with streaming data to work in congested networks.

At the same time, the US government is addressing spectrum shortfalls by launching an action program that will free up 500 MHz of spectrum in the next 10 years. This program was proposed on June 28, 2010, and its goal is to provide wireless technology in the new frequency bands, as well as providing full high-speed coverage in urban and suburban areas [22]. As part of this project, on September 23, 2010, the FCC provided access to nearly 200 MHz of ultra-high (VHF) and ultra-high (UHF) spectrum frequencies for unlicensed use, called “unused spectrum” [23]. The only limitation when working with these frequency bands is a ban on creating harmful interference, since existing wireless microphone devices are already operating in the same band. In this regard, on July 22, 2011, the IEEE 802.22 working group completed the development of a standard for new wireless systems using cognitive radio technology (or “spectrum understanding”), a key feature of which is dynamic spectrum tracking and operation at accessible frequencies, which avoids the occurrence hazardous interference with coexisting wireless devices [24]. There has recently been a debate about the designation of part of the unused spectrum for licensed use and the auctioning of this part of the spectrum [25].

The coexistence of unlicensed devices within the same frequency bands and the disagreement over unlicensed or licensed use of the spectrum are two major problems that have arisen in developing FCC spectrum assignment programs over several years. For example, in an unused spectrum, the coexistence of wireless microphones and wireless communication devices is possible thanks to cognitive radio technology. However, compared with other spatial processing technologies (such as DIDO), cognitive radio can provide only a fraction of spectrum efficiency. Similarly, the performance of WiFi systems over the past few decades has deteriorated significantly due to an increase in the number of access points, and also because Bluetooth / ZigBee devices operating in the same unlicensed ISM band generate uncontrolled interference. One of the drawbacks of the unlicensed spectrum is the unregulated use of RF devices that will continue to clog the spectrum in subsequent years. RF clogging also prevents the future use of unlicensed spectrum in licensed operations, which will limit the important market opportunities for wireless broadband commercial services and spectrum auctions.

We offer a new system and methods that allow you to dynamically distribute the wireless spectrum, as well as allowing various services and standards to coexist and develop. One embodiment of our method dynamically assigns RF transceivers the right to use certain parts of the spectrum and allows the gradual obsolescence of these same RF devices, which will provide the following features.

i) Spectrum regulation for new types of wireless operations (i.e. licensed or unlicensed) and / or compliance with new restrictions on radiated RF power. This feature allows you to conduct spectrum auctions whenever it becomes necessary, and it is not necessary to decide in advance whether to use it as an unlicensed or licensed spectrum. It also allows you to adjust the transmit power levels to the new radiated power levels assigned by the FCC.

ii) Coexistence of different technologies operating in the same band (i.e. unused spectrum and wireless microphones, WiFi and Bluetooth / ZigBee) so that the bands can be dynamically reassigned when new technologies appear, while avoiding interference with existing technologies .

iii) Comprehensive development of wireless infrastructure with the transition of systems to more advanced technologies that provide higher spectral efficiency, greater coverage and better quality of work to support new types of services that require higher QoS (i.e. streaming high-quality video).

Next, we describe the system and method of planned development and obsolescence of the multi-user spectrum. One embodiment of the system includes one or a plurality of centralized processors (CP) 4901-4904 and one or a plurality of distributed nodes (DNs) 4911-4913 communicating via a wired or wireless connection, as shown in FIG. 49. For example, in the context of 4G-LTE networks [26], the centralized processor is an internal access gateway (ACGW) connected to several transceivers of node B. In the context of WiFi, the centralized processor is an Internet service provider (ISP), and distributed nodes are WiFi access points connected to ISP via modems or direct cable or DSL connection. In another embodiment of the present invention, the system is a distributed input-distributed output (DIDO) system [0002-0009] with one centralized processor (or BTS) and distributed nodes representing DIDO access points (or distributed DIDO antennas connected to the BTS through BSN).

DN 4911-4913 interact with CP 4901-4904. The information that the DN exchanges with the CP is used to dynamically control the configuration of nodes for the evolving network architecture configuration. In one embodiment, DN 4911-4913 communicate their CP identification numbers. CPs store the identification numbers of all DNs connected through the network in a lookup table or in a shared database. Such lookup tables or databases can be shared by other CPs, and this data is synchronized in such a way that all CPs have access to the latest information about all DNs in the network.

For example, the FCC may decide to allocate a certain portion of the spectrum for unlicensed use, and the proposed system may be configured to operate within that spectrum. Due to spectrum limitations, the FCC may subsequently designate part of this spectrum for licensed use by commercial carriers (i.e., AT&T, Verizon, or Sprint), defense agencies, or state security agencies. In standard wireless systems, such coexistence is not possible, as existing wireless devices operating in the unlicensed frequency band can cause harmful interference in licensed RF transceivers. In our system, the distributed nodes exchange control information with CP 4901-4903 to adapt their RF transmission to a changing frequency band pattern. In one embodiment, DN 4911-4913 was initially configured to operate in different frequency bands within the available spectrum. When the FCC designates one or many parts of such a spectrum for licensed use, CPs exchange control information with unlicensed DNs and retune them to close frequency bands intended for licensed use so that unlicensed DNs do not cause interference in licensed DNs. This scenario is presented in FIG. 50, where unlicensed nodes (for example, 5002) are shown by shaded circles, and licensed nodes are shown by empty circles (for example, 5001). In another embodiment, the entire spectrum is assigned to a new licensed service, and the CPs use the control information to disable all unlicensed DNs to avoid interference with licensed DNs. This scenario is presented in FIG. 51, where obsolete unlicensed sites are crossed out with crosses.

In another example, it may be necessary to limit the radiated power for certain devices operating on a certain frequency band in order to comply with the exposure limits set by the FCC [27]. For example, a wireless system can initially be developed for fixed wireless communication lines, where DN 4911-4913 are connected to outdoor transceiver antennas on the roof. In the future, the same system can be updated to support DN with indoor portable antennas to provide better indoor coverage. The restrictions on the effects of portable devices adopted by the FCC are more stringent than the restrictions on transmitters installed on roofs, due to the close proximity of such devices to the human body. In this case, old DNs designed for outdoor use can be reused for indoor use provided that the transmission power settings are adjusted. In one embodiment of the present invention, the DNs are designed with predefined sets of transmit power levels, and the CP 4901-4903 sends control information to DN 4911-4913 to select new power levels in the event of a system upgrade, thus meeting FCC exposure limits. In another embodiment, DNs are made with only one radiated power setting, and such DNs that exceed new radiated power levels are remotely disabled by the CP.

In one embodiment, CP 4901-4903 periodically checks all DN 4911-4913 in the network to determine their right to operate as RF transceivers in accordance with a particular standard. Non-updated DNs can be marked as outdated and removed from the network. For example, DNs operating within the current power limit and frequency band continue to be active on the network, and all others are turned off. It should be noted that the DN parameters are controlled by CP and are not limited to radiated power and frequency band; These parameters may include any other parameters that define the wireless communication line between the DN and the client device.

In another embodiment, DN 4911-4913 can be reconfigured to allow coexistence of systems operating according to different standards within the same spectrum. For example, radiated power, bandwidth, or other settings for a specific DN that works in the context of a WLAN can be adjusted to accommodate new DNs designed to work with WPAN while avoiding harmful interference.

When new wireless standards are developed to increase the speed of data transfer and coverage in wireless networks, DN 4911-4913 can be updated to support these standards. In one embodiment, DNs are software-defined radio systems (SDRs) equipped with programmable computing functionality such as FPGAs, DSPs, CPUs, GPUs and / or GPGPUs that run baseband signal processing algorithms. If the standard is updated, new algorithms for the main frequency band can be remotely downloaded from CP to DN to comply with this standard. For example, in one embodiment, the first standard is based on CDMA, and then it is replaced by OFDM technology to support various types of systems. Similarly, the sampling step, power, and other parameters in the DN can be updated remotely. The SDR element in DN allows the network to be continuously updated as new technologies are developed and thus improve overall system performance.

In another embodiment, the described system is a cloud-based wireless system comprising multiple CPs, distributed nodes, and a network connecting the CP and the DN. In FIG. 52 illustrates one example of a cloud-based wireless system in which nodes denoted by filled circles (e.g., 5203) communicate with CP 5206, nodes denoted by empty circles interact with CP 5205, and CP 5205-5206 communicate with each other via network 5201. In one embodiment of the present invention, the cloud-based wireless system is a DIDO system, and the DNs are connected to the CP and exchange information to periodically or instantly change system parameters and dynamically adjust to changing conditions. wired architecture. In a DIDO system, CPs are BID DIDOs, distributed nodes are DIDO distributed antennas, a network is BSNs, and many BTSs are interconnected via a centralized DIDO processor, as described in our previous patent applications [0002-0009].

All DN 5202-5203 in a cloud-based wireless system can be grouped into different sets. Such sets of DNs can simultaneously create non-interfering wireless links for a large number of client devices, each set supporting a variety of multiple access technologies (e.g., TDMA, FDMA, CDMA, OFDMA and / or SDMA), different modulation schemes (e.g. , QAM, OFDM) and / or coding (e.g., convolutional coding, low density parity check code (LDPC), turbo codes). Similarly, each client can be served using various multiple access technologies and / or various modulation / coding schemes. Based on the active clients in the system and the standards that they use for wireless communication lines, CP 5205-5206 dynamically selects a subset of DN that supports these standards and is in the range of client devices.

Literature

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[14] 3GPP, “LTE”, http://www.3gpp.org/LTE

[15] Motorola, “Long Term Evolution (LTE): A Technical Overview”, http: business.motorola.com/experiencelte/pdf/LTETechnicalOverview.pdf

[16] Federal Communications Commission, “Authorization of Spread Spectrum Systems Under Parts 15 and 90 of the FCC Rules and Regulations,” June 1985.

[17] ITU, “ISM band” http://www.itu.int/ITU-R/terrestrial/faq/index.html#g013

[18] S. Perlman and A. Forenza, “Distributed-input distributed-output (DIDO) wireless technology: a new approach to multiuser wireless,” August 2011.

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[23] FCC, Open Commission meeting, September 23, 2010

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[24] IEEE 802.22, “IEEE 802.22 Working Group on Wireless Regional Area Networks,” http://www.ieee802.org/22/

[25] A bill, 112th congress, 1 ^st session, July 12, 2011

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[27] FCC, “Evaluating compliance with FCC guidelines for human exposure to radiofrequency electromagnetic fields”, OET Bulletin 65, revised 97-01, August 1997.

V. Systems and methods for combating the effects of Doppler in wireless systems with distributed input - distributed output

In this part of the detailed description, we consider multi-user (MU) multi-antenna systems (MAS) for multi-user wireless transmission, adaptively changing the configuration of parameters to compensate for the Doppler effect resulting from user movement or changes in the distribution environment. In one embodiment, the MAS. is a DIDO system described in co-pending patent applications [0002-0016] and shown in FIG. 53. The DIDO system in one embodiment includes the following components.

- User equipment (UE). In one embodiment, the UE 5301 includes an RF receiver for fixed or mobile clients receiving data streams through the downlink (DL) channel of the DIDO transport network and sending data through the uplink (UL) channel of the DIDO transport network.

- Base Transceiver Station (BTS). BTS 5310-5314 in one embodiment, communicate with a DIDO transport link through a wireless channel. BTS 5310-5314 are access points containing a DAC / ADC and a radio frequency (RF) circuit for converting a modulating signal to RF. In some cases, the BTS is a simple RF transceiver equipped with a power amplifier / antenna, and the RF signal is transmitted to the BTS using the “RF channel over fiber” technology, as described in our patent application.

- Controller (CTR). CTR 5320 in one embodiment is a specific type of BTS designed to perform special functions, such as transmitting training signals to synchronize BTS and / or UEs in time / frequency, receiving / transmitting control information from / to UEs, receiving channel status information (CSI) or channel quality information from the UE.

- Centralized processor (CP). The CP 5340, in one embodiment, is a DIDO server communicating with the Internet or other types of 5350 external networks via a DIDO transport communication network. The CP computes the information processing in the DIDO baseband and sends signals to the distributed BTS for DL transmission.

- Network of base stations (BSN). BSN 5330, in one embodiment, is a network connecting CPs and distributed BTSs, transmitting data for either the DL channel or the UL channel. A BSN is a wired network, a wireless network, or a combination thereof. For example, a BSN is a DSL, cable, fiber optic network, or wireless link within or outside line of sight. Additionally, the BSN is a private network, a local area network, or the Internet.

As described in co-pending applications, the DIDO system creates independent channels for multiple users so that each user receives interference-free channels. In DIDO systems, this is achieved by using distributed antennas or BTS to exploit spatial diversity. In one embodiment, the DIDO system is configured to use spatial, polarization, and / or pattern diversity to increase the number of degrees of freedom in each of the channels. More degrees of freedom in a wireless communication link are used to transmit independent data streams to a larger number of UEs (i.e., to increase multiplexing), and / or to improve coverage (i.e., to increase diversity).

BTS 5310-5314 are located in convenient places where there is access to the Internet or BSN. In one embodiment of the present invention, UE 5301-5305 are arranged randomly between, around and / or surrounded by BTS or distributed antennas, as shown in FIG. 54.

In one embodiment, BTS 5310-5314 transmit a training signal and / or independent data streams to UE 5301 via a DL channel, as shown in FIG. 55. UEs use a training signal for various purposes, such as time / frequency synchronization, channel estimation, and / or channel status information (CSI) estimation. In one embodiment of the present invention, the DL in the MU-MAS is configured to use non-linear precoding, such as dirty-paper coding (DPC) [1-2] or Tomlinson-Harashima precoding (TH) [3-4]. In another embodiment of the present invention, the DL in the MU-MAS is configured to use non-linear precoding, for example block diagonalization (BD) precoding, as described in co-pending patent applications [0003-0009], or beamforming with zero position control (ZF-BF) [5]. If the number of BTSs is greater than the number of UEs, additional BTSs are used to increase the quality of the communication link for each UEs by diversity schemes such as antenna selection or eigenmode selection described in [0002-0016]. If the number of BTS is less than the number of UEs, the additional UEs share wireless links with other UEs using standard multiplexing technologies (e.g., TDMA, FDMA, CDMA, OFDMA).

The UL channel is used to transfer data from the UE 5301 to the CP 5340 and / or CSI (or channel quality information) used by the DIDO precoding device. In one embodiment, the UL channels from the UE are multiplexed by standard multiplexing technologies (e.g., TDMA, FDMA, CDMA, OFDMA) to the CTR, as shown in FIG. 56, or to the nearest BTS. In another embodiment of the present invention, spatial processing techniques are used to separate the UL channels from the UE 5301 to the distributed BTS 5310-5314, as shown in FIG. 57. For example, UL streams are transmitted from the client to the DIDO antennas through multiple input multiple output multiplexing (MIMO) multiplexing schemes. MIMO multiplexing schemes include independent data streams transmitted by clients and use linear or non-linear receivers on DIDO antennas to eliminate interference between channels. In another embodiment, downlink weights in the uplink are used to demodulate the upstreams, provided the UL / DL channels are reversible and the channel does not change much between DL and UL transmission due to the Doppler effect. In another embodiment, an downlink receiver (MRC) is used in the downlink to increase the quality of the signal from each client in the DIDO antennas.

Data, control information, and CSI sent over DL / UL channels are shared between CP 5340 and BTS 5310-5314 via BSN 5330. Known training signals for DL channel can be stored in BTS 5310-5314 to reduce overhead in BSN 5330. B Depending on the type of network (i.e. wireless or wired, DSL, cable or fiber), the data transfer speed through the BSN 5330 may not be enough for the exchange of information between the CP 5340 and the BTS 5310-5314, especially when the baseband signal is sent to the BTS . For example, suppose that BTS transmits independent data streams to each UE at a speed of 10 Mbit / s over a 5 MHz band (due to digital modulation and the FEC coding scheme used in the wireless communication line). If 16 quantization bits are used for the real component and 16 for the imaginary component, the baseband signal requires 160 Mbps of bandwidth from CP to BTS via BSN. In one embodiment, the CP and BTS are equipped with encoding and decoding devices for compressing and decompressing information sent through the BSN. On the forward link channel, pre-encoded baseband data sent from the CP to the BTS is compressed to reduce the number of bits and overhead in the BSN. Similarly, in the reverse channel, the CSI and data (sent through the uplink from the UE to the BTS) are compressed before being transmitted from the BTS to the CP via the BSN. Various compression algorithms are used to reduce the number of bits and overhead in the BSN, including, without limitation, lossless and / or lossy methods [6].

One feature of DIDO systems used in one embodiment is the receipt of CP 5340 CSI or channel quality information between all BTS 5310-5314 and UE 5301, which allows precoding. As explained in [0006], DIDO performance depends on the rate at which CSI is transmitted to the CP, relative to the rate of change of the wireless links. It is well known that the oscillations of the complex channel gain occur due to the mobility of the UE and / or changes in the propagation medium causing the Doppler effect. The rate of change in the channel is measured in units of channel coherence time (T _s ) inversely proportional to the maximum Doppler shift. In order for DIDO transmissions to be successful, the delay due to CSI feedback must be a fraction of (for example, 1/10 or less) of the channel coherence time. In one embodiment, the delay in the CSI feedback loop is measured as the duration between the moment of sending the CSI training signal and the moment of demodulating the pre-encoded data on the UE side, as shown in FIG. 58.

In frequency division duplex (FDD) systems, the DIDO BTS 5310-5314 send CSI training signals to the UE 5301, configured to evaluate CSI and send feedback to the BTS. The BTS then sends the CSI via the BSN to the CP 5340, which processes the pre-encoded DIDO data streams and sends them back to the BTS via the BSN 5330. Finally, the BTS sends the pre-encoded streams to the UE demodulating the data. In the case of FIG. 58, the total delay in the DIDO feedback loop is:

2 * T _DL + T _UL + T _BSN + T _CP ,

where T _DL and T _UL include the time to create, send, and process the downstream and upstream data groups, respectively, T _BSN represents the round trip delay of the signal on the BSN, and T _CP represents the time C _P spent on CSI processing, generating precoded data streams for the UE and scheduling various UEs for the present transmission. In this case, T _{DL is} multiplied by 2 to take into account the travel time of the training signal (from BTS to UE) and the travel of the feedback signal (from UE to BTS). In a time division duplex (TDD) system, if channel reversibility is possible, the first stage is skipped (i.e., transmitting the CSI training signal from the BSI to the UE), since the UE sprinkles the CSI training signal to the BTS computing the CSI and sending it to Wed Therefore, in such an embodiment, the total delay in data delivery along the DIDO feedback loop is:

T _DL + T _UL + T _BSN + T _CP .

The data transfer delay T _BSN depends on the type of BSN: whether you use a dedicated cable, DSL, fiber optic connection or regular Internet. Typically, values range from 1 to 50 ms. The computation time in CP can be reduced if DIDO processing in CP is performed on specialized processors such as ASIC, FPGA, DSP, CPU, GPU and / or GPGPU. Moreover, if the number of BTS 5310-5314 exceeds the number of UE 5301, then all UEs can be serviced at the same time, which will remove the delay that occurs during multi-user distribution. Therefore, the delay T _{CP is} negligible compared to T _BSN . So, the processing of transmitting and receiving in the downstream and upstream channels is usually performed on ASICs, FPGAs or DSPs with a short calculation time, and if the frequency band of the signal is relatively large (for example, more than 1 MHz), the frame duration can be very short (i.e. less than 1 ms). Therefore, T _DL and T _{UL are} also small compared to T _BSN .

In one embodiment of the present invention, the CP 5340 monitors the Doppler speeds of all UE 5301 and dynamically assigns the BTS 5310-5314 with the lowest T _BSN to the UE with the highest Doppler speed. This setting is based on various criteria.

- Type BSN. For example, dedicated fiber links tend to have less latency than cable modems or DSLs. In this case, lower latency BSNs are used for highly mobile UEs (e.g., motorways, trains), while higher latency BSNs are used for fixed wireless or low mobility UEs (e.g. home equipment, pedestrians, cars in residential areas).

- Type QoS. For example, a BSN may support various types of DIDO or non-DIDO traffic. You can define quality of service (QoS) with different priorities for different types of traffic. For example, BSN assigns high priority to DIDO traffic and low priority to non-DIDO traffic. In an alternative embodiment, high priority QoS is assigned to traffic for high mobile UEs, and low priority QoS is assigned to low mobile UEs.

- Long-term statistics. For example, traffic transmitted via BSN can vary significantly depending on the time of day (for example, at night in residential buildings and during the day at offices). A higher traffic load may result in a longer delay. In this case, at different times of the day, BSNs with high traffic, if this leads to high latency, are used for low mobile UEs, while BSNs with lesser traffic, if this leads to low latency, are used for highly mobile UEs.

- Short-term statistics. For example, each BSN may be affected by temporary network congestion, which can lead to high latency. In this case, the CP can adaptively select BTS from the congested BSNs if the congestion causes a large delay for low mobile UEs, and from the remaining BSNs, if they have a lower delay, for highly mobile UEs.

In another embodiment of the present invention, the BTS 5310-5314 is selected based on the occurring Doppler bias in each individual BTS-UE link. For example, on a line of sight (LOS) B in FIG. 59, the maximum Doppler shift is a function of the angle (φ) between

the communication line BTS-UE and the transport speed (v) in accordance with the known equation:

where λ is the wavelength corresponding to the carrier frequency. Thus, in LOS channels, the Doppler shift is maximum for link A and almost zero for link C in FIG. 59. In non-LOS systems (NLOS), the maximum Doppler shift depends on the direction of the multi-paths around the UE, but mainly because the BTS in DIDO systems are distributed, some BTS will have a higher Doppler shift for a given UE ( for example, BTS 5312), while other BTS will have a lower Doppler offset for a given UE (for example, BTS 5314).

In one embodiment, the CP monitors the Doppler speed in each communication line of the BTS-UE and selects only the lines with the lowest Doppler effect for each UE. Similar to the technologies described earlier in the document, the CP 5340 assigns a “user cluster” for each UE 5301. The user cluster is a set of BTS with good communication quality (determined based on a specific threshold signal-to-noise ratio, SNR) to the UE and with low Doppler bias (determined, for example, based on a predetermined threshold value), as shown in FIG. 60. In FIG. 60 BTSs 5 through 10 have a good SNR to UE1, but only BTSs 6 through 9 have a small Doppler effect (i.e., less than a given threshold value).

The CP in the present embodiment registers all SNR and Doppler bias values for each BTS-UE link in the matrix, and for each UE selects a submatrix satisfying the threshold SNR and Doppler bias values. In the example of FIG. 61, the submatrix is highlighted by a green dashed line surrounding C _2.6 , C _2.7 , C _3.9 , C _4.7 , C _4.8 , C _4.9 , and C _5.6 . DIDO precoding weights are calculated for such a UE based on such a submatrix. It should be noted that BTS 5 through 10 are available for UEs 2, 3, 4, 5, and 7, as shown in the table in FIG. 61. Further, in order to avoid interference in UE1 when transmitting to other UEs, BTSs 5 through 10 must either be turned off or assigned to other orthogonal channels based on standard multiplexing technologies such as TDMA, FDMA, CDMA or OFDMA.

In another embodiment, the negative impact of the Doppler effect on the characteristics of pre-coding DIDO systems is reduced by linear prediction, a technology that estimates future complex channel coefficients based on previous estimates. As an example (but not as a limitation), various prediction algorithms for wireless systems with a single input - a single output (SISO) and OFDM systems were proposed in [7–11]. Knowing the future complex channel coefficients, you can reduce the error that occurs due to outdated CSI. For example, in FIG. 62 shows channel gain (or CSI) at various points in time: i) t _CTR is the point in time at which the CTR in FIG. 58 receives CSI from a UE in FDD systems (or, similarly, BTSs are configured to evaluate CSI in a UL channel using DL / UL reversibility in TDD systems); ii) t _CP is the point in time at which the CSI is delivered to the CP through the BSN; iii) T _BTS is the point in time at which CSI is used for precoding on the wireless link. In FIG. 62 it can be seen that due to the delay T _BSN (also shown in FIG. 58), the CSI estimated at time t _CTR will be outdated (i.e., the complex channel gain has changed) by the time it is used in wireless transmission over the DL channel at time t _BTS . One way to avoid such an effect due to Doppler shift is to start the prediction method on SR. The CSI estimates available in the CP at the time t _CTR have a delay T _BSN / 2 due to the delay CTR to the CP and correspond to the channel gain at time t ₀ in FIG. 62. Next, the CP uses part or all of the CSI estimated before t ₀ and stored in memory to predict future channel coefficients at t ₀ + T _BSN = t _CP . If the prediction algorithm has minimal error propagation, the predicted CSI at time point t _CP reliably reflects channel gain in the future. The time difference between the predicted CSI and the present CSI is called the “prediction horizon” and in SISO systems it is usually scaled using channel coherence time.

In DIDO systems, the prediction algorithm is more complex, since it is capable of estimating future channel coefficients in both the time and spatial domains. Linear prediction algorithms use the spatio-temporal characteristics of MIMO wireless channels described in [12–13]. It was shown in [13] that the performance of prediction algorithms in MIMO systems (measured by standard error, standard deviation) improves with a high channel coherence time (ie, with a reduced Doppler effect) and a low channel coherence length (due to lower spatial correlation) . Therefore, the prediction horizon (expressed in seconds) of space-time methods is directly proportional to the channel coherence time and inversely proportional to the channel coherence length. In DIDO systems, the coherence length is low due to the high spatial selectivity generated by distributed antennas.

The prediction methods described herein use the temporal and spatial diversity of DIDO systems to predict the future channel vector (i.e., CSI from BTS to UE). The present embodiments utilize the spatial diversity available in wireless channels to obtain a small CSI prediction error and an extended prediction horizon using existing SISO and MIMO prediction algorithms. One of the important features of these methods is the use of distributed antennas, taking into account the receipt of uncorrelated complex channel coefficients from distributed UEs.

In one embodiment of the present invention, a spatial and temporal predictive device is combined with a frequency domain estimator to allow CSI prediction on all available subcarriers in a system, such as OFDM systems. In another embodiment of the present invention, DIDO precoding weights (rather than CSI) are predicted based on previous DIDO weight estimates.

Literature

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[3] M. Tomlinson, “New automatic equalizer employing modulo arithmetic”, Electronics Letters, pp. 138-139, March 1971.

[4] H. Miyakawa and H. Harashima, “A method of code conversion for digital communication channels with intersymbol interference”, Transactions of the Institute of Electronic

[5] R.A. Monziano and T.W. Miller, Introduction to Adaptive Arrays, New York: Wiley, 1980

[6] Guy E. Blelloch, introduction to Data Compression, ”Carnegie Mellon University Tech. Report, September 2010

[7] A. Duel-Hallen, S. Hu, and H. Hallen, “Long-Range Prediction of Fading Signals,” IEEE Signal Processing Mag., Volume 17, No. 3, pp. 62-75, May 2000.

[8] A. Forenza and R.W. Heath, Jr., “Link Adaptation and Channel Prediction in Wireless OFDM Systems,” in Proc. IEEE Midwest Symp. on Circuits and Sys., August 2002, pp. 211-214.

[9] M. Sternad and D. Aronsson, “Channel estimation and prediction for adaptive OFDM downlinks [vehicular applications],” in Proc. IEEE Vehicular Technology Conference, Volume 2, October 2003, pp. 1283-1287.

[10] D. Schafhuber and G. Matz, “MMSE and Adaptive Prediction of Time-Varying Channels for OFDM Systems,” IEEE Trans. Wireless Commun., Volume 4, No. 2, pp. 593-602, March 2005

[11] I.S. Wong and B.L. Evans, “Joint Channel Estimation and Prediction for OFDM Systems,” in Proc. IEEE Global Telecommunications Conference, St. Louis, Missouri, December 2005

[12] M. Guillaud and D. Slock, “A specular approach to MIMO frequency selective channel tracking and prediction,” in Proc. IEEE Signal Processing Advances in Wireless Communications, July 2004, pp. 59-63.

[13] Wong, I.C. Evans, B.L., “Exploiting Spatio-Temporal Correlations in MIMO Wireless Channel Prediction,” IEEE Globecom Conf., Pp. 1-5, December 2006.

Embodiments of the present invention may include various steps as described above. Such stages can be implemented in computer-executable instructions, forcing the universal or specialized processors to perform certain stages. For example, the various components within the base stations / APs and client devices described above can be implemented as software running on universal or specialized processors. In order not to complicate the understanding of the aspects related to the present invention, various known components of a personal computer, such as a computer memory device, a hard disk, data input devices, etc., are not shown in the figures.

In an alternative embodiment, the various function modules shown in this document and related steps can be performed by special hardware components that contain hardware logic to complete the steps, such as a specialized application integrated circuit ("ASIC"), or any combination of programmed computer components and custom hardware components.

In one embodiment, certain modules, such as the coding, modulation, and logic processing modules 903 described above, may be implemented on a programmable digital signal processor (“DSP”) (or group of DSPs), such as a DSP using the Texas Instruments TMS320x architecture ( e.g. TMS320C6000, TMS320C5000, ... etc.). The DSP in this embodiment can be integrated into an expansion card for a personal computer, for example a PCI card. Of course, many different DSP architectures can be applied if they are consistent with the fundamental principles of the present invention.

Elements of the present invention can also be implemented as a computer-readable medium for storing computer-executable instructions. Such computer-readable media may include, but are not limited to, flash memory, optical disks, CD-ROMs, DVD-ROMs, RAM, EPROM, EEPROMs, magnetic or optical cards, distribution media, or other types of computer-readable media suitable for storing electronic commands. For example, the present invention can be downloaded as a computer program transmitted from a remote computer (eg, server) to a requesting computer (eg, client) via data signals implemented as a carrier wave or other propagation medium via a communication line (eg, modem or network connection )

In the description provided, for purposes of explanation, numerous specific details have been set forth to provide a thorough understanding of the presented system and method. Specialists in the art should understand that the system and method can be implemented without some of these specific details. Accordingly, the scope and essence of the present invention must be considered from the point of view of the claims below.

In addition, numerous publications have been cited throughout the foregoing description to provide a more complete understanding of the present invention. All of these cited publications are incorporated herein by reference.

Claims (49)

1. A multi-user (MU) multi-antenna system (MAS), comprising: one or more centralized units connected to communicate with a plurality of distributed transceiver stations or antennas via a network; a network consisting of wired or wireless communication lines, or a combination thereof, used as a communication channel of a transport network; wherein one or more centralized units are configured to communicate with a plurality of distributed transceiver stations or antennas for adaptively reconfiguring simultaneous communication in a single frequency band between distributed transceiver stations or antennas and a plurality of users to reduce the effect of Doppler effects resulting from user movements or changes in the data distribution environment. 2. The system of claim 1, wherein the MU-MAS system comprises one or more sets of user equipment (UE), base transceiver stations (BTS), controllers (CTR), centralized processors (CP), and base station networks (BSN). 3. The system of claim 1, comprising distributed antennas using spatial, polarization, and / or pattern diversity to increase data rate and / or coverage for one or many users in wireless systems. 4. The system of claim 1, wherein the UEs are located around or between distributed antennas or surrounded by distributed antennas. 5. The system of claim 1, wherein the MU-MAS is configured to use complex weights in the uplink receiver to demodulate independent streams (eg, data or CSI) from the UE. 6. The system of claim 5, wherein the complex weights of the uplink receiver are obtained from pre-coded downlink weights or calculated by the optimal addition receiver. 7. The system of claim 2, wherein the CP and BTS are equipped with encoding / decoding devices for compressing / decompressing the information that they exchange through the BSN. 8. The system of claim 7, wherein the pre-encoded baseband data streams are compressed before transmitting from / to the BTS to / from the distributed MU-MAS antennas to reduce overhead in the BSN. 9. The system of claim 2, wherein the CP is adapted based on a delay in the BSN to adaptively select BTSs to be used for low or high mobile UEs. 10. The system of claim 9, wherein the adaptation is based on the type of (high or low) data rate in the BSN, or QoS, or average traffic statistics (for example, using different networks in the daytime or at night), or current traffic statistics ( e.g. temporary network congestion) in the BSN. 11. The system of claim 2, wherein the CP is configured to adaptively select BTSs to be used for low- or high-mobility UEs based on Doppler speeds in each communication line of the BTS-UE. 12. The system of claim 2, characterized in that it is capable of applying linear prediction to estimate CSI or MU-MAS precoding weights, thereby eliminating the adverse Doppler effect affecting the performance of the MU-MAS system. 13. The system according to p. 12, characterized in that it is made with the possibility of applying prediction in the time, frequency and / or spatial domains. 14. A method of reducing the effect of Doppler effects implemented for a multi-user (MU) multi-antenna system (MAS), the MU-MAS system comprising at least one centralized unit coupled to communicate with a plurality of distributed transceiver stations (BTS) or antennas, the method contains stages in which: measuring the Doppler speed of the first mobile user with respect to the plurality of BTS; and the central unit is connected to a plurality of BTS over a network for adaptively reconfiguring simultaneous communication in a single frequency band between a plurality of BTS and a plurality of users, while in the adaptive reconfiguration step, a first mobile user is dynamically assigned to a first BTS or a first set of BTS from a plurality of BTS based on the measured Doppler speed for the first BTS relative to other BTS. 15. The method of claim 14, wherein if the measured Doppler speed of the first mobile user is relatively higher than the measured Doppler speed of the second mobile user, then the dynamic assignment includes assigning the first mobile user to the first BTS or the first set of BTS and assigning the second mobile user to the second BTS or a second set of BTS, wherein the first BTS or first set of BTS has a relatively lower associated delay than the second BTS or second set of BTS. 16. The method of claim 15, wherein the delay includes: a time for transmitting a first training signal from a first mobile user to a BTS, a two-way delay in a base station network (BSN) connecting the BTS and a centralized processor (CP), and time, CP spent on processing channel state information (CSI) in a wireless channel between the BTS and the first mobile user, generating precoded data streams for the first mobile user based on CSI and scheduling transmissions to various mobile users, including I am the first user for the current transfer. 17. The method of claim 16, wherein the delay further includes transmitting a second training signal from the BTS to the first mobile user. 18. The method of claim 14, wherein the dynamic assignment step further performs an assignment based on a combination of link quality in the communication channel between each BTS and the first mobile user and the measured Doppler speed associated with each BTS relative to the first user. 19. The method according to p. 18, in which for a given Doppler speed selected BTS with a relatively higher quality of the communication line. 20. The method according to p. 18, in which for a given quality of the communication line selected BTS with a relatively lower Doppler speed. 21. The method of claim 14, further comprising the step of: evaluate future complex channel coefficients based on previous complex channel coefficients to compensate for the negative impact of the Doppler effect on the communication between the BTS and the first mobile user. 22. The method of claim 21, wherein linear prediction is used for estimation. 23. The method according to p. 14, in which the first mobile user is dynamically assigned to the first BTS or the first set of BTS based on Doppler speed and channel status information (CSI), which determines the quality of communication channels between the mobile user and each BTS from the set of BTS. 24. The method of claim 23, further comprising the steps of: constructing a matrix of Doppler velocities and link quality for each BTS from the plurality of BTS relative to the first mobile user; and choose a BTS whose Doppler speed is below a predetermined threshold value, and the quality of the communication line is above a predetermined threshold value. 25. Multi-user (MU) multi-antenna system (MAS) to reduce the effects of Doppler effects, containing: multiple base transceiver stations (BTS); a network connecting a plurality of BTSs to at least one centralized processor (CP); the first mobile user establishing a communication line with each of the BTS; moreover, a centralized processor (CP) is configured to communicate with a plurality of BTS over a network for adaptively reconfiguring simultaneous communication in a single frequency band between a plurality of BTS and a plurality of users, wherein the adaptive reconfiguration includes measuring the Doppler speed of the first mobile user relative to each BTS of the plurality BTS and the dynamic assignment of the first mobile user of the first BTS from the set of BTS based on the measured Doppler speed for the first BTS rel respect to other BTS. 26. The system of claim 25, wherein if the measured Doppler speed of the first mobile user is relatively higher than the measured Doppler speed of the second mobile user, then the dynamic assignment includes assigning the first mobile user to the first BTS and assigning the second mobile user to the second BTS, wherein the first BTS has a relatively lower associated delay than the second BTS. 27. The system of claim 26, wherein the delay includes: a time for transmitting a first training signal from a first mobile user to a BTS, a two-way delay in a base station network (BSN) connecting the BTS and a centralized processor (CP), and time, the required CP for processing channel state information (CSI) for the wireless channel between the BTS and the first mobile user, generating precoded data streams for the first mobile user based on CSI and scheduling transmissions to various mobile users, including tea of the first user for the current transfer. 28. The system of claim 27, wherein the delay further includes transmitting a second training signal from the BTS to the first mobile user. 29. The system of claim 25, wherein the dynamic assignment further comprises an assignment based on a combination of link quality in the communication channel between each BTS and the first mobile user and the measured Doppler speed associated with each BTS relative to the first user. 30. The system of claim 29, wherein, for a given Doppler speed, a BTS with a relatively higher quality link is selected. 31. The system of claim 29, wherein, for a given link quality, BTS is selected with a relatively lower Doppler speed. 32. The system of claim 25, wherein the CP is configured to evaluate future complex channel coefficients based on previous complex channel coefficients to compensate for the negative effect of the Doppler effect on communication between the BTS and the first mobile user. 33. The system of claim 32, wherein the linear prediction is used for estimation. 34. The system of claim 25, wherein the first mobile user is dynamically assigned to the first BTS based on Doppler speed and channel status information (CSI), which determines the quality of the communication channel between the mobile user and each BTS from the set of BTS. 35. The system of claim 34, wherein the CP is configured to perform additional actions: on the construction of a matrix of values of Doppler speeds and quality of communication lines for each BTS from the set of BTS relative to the first mobile user; and optionally BTS, the Doppler speed of which is below a predetermined threshold value, and the quality of the communication line is above a predetermined threshold value.

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