HK1117300A - Systems and methods for reducing uplink resources to provide channel performance feedback for adjustment of downlink mimo channel data rates - Google Patents

Description

System and method for reducing uplink resources required to provide channel performance feedback for adjusting downlink MIMO channel data rates

Technical Field

The present invention relates generally to wireless communication systems, and more particularly to a system and method for reducing the amount of feedback required to select an appropriate data rate for encoding a data stream in order to maximize data throughput.

Background

A wireless communication system may include a plurality of base stations and a plurality of mobile stations. A particular base station may be communicating with one or more mobile stations at any given moment. In general, communication from a base station to a mobile station is referred to as a forward link or downlink, and communication from a mobile station to a base station is referred to as a reverse link or uplink.

Data to be transmitted between a base station and a mobile station is typically encoded, transmitted by a transmitter (in either the base station or the mobile station), received by a receiver (in either the base station or the mobile station), and then decoded. The data is encoded at a data rate selected based on the quality of the communication link. The better the link, the higher the data rate that can be used.

Although base stations are generally capable of increasing the power at which data is transmitted and thus the channel quality, this may not always be desirable. For example, if the quality of the communication link is sufficient to support the appropriate data rate, increasing the power may only increase the mutual interference with other communications. Thus, the base station typically implements some mechanism to control the power and data rate at which data is transmitted. This may involve, for example: measuring performance (such as signal-to-noise ratio or SNR) at the mobile station; providing feedback regarding the performance to the base station; and changing a data rate at which the data is encoded and transmitted based on the measured performance.

One of the recent advances in the field of wireless communication is the development of MIMO (multiple input multiple output) systems. MIMO systems use multiple transmit antennas and multiple receive antennas to establish multiple channels that can be spatially distinguished from one another. One of the problems encountered in developing communications utilizing MIMO technology is: the throughput of each MIMO channel is maximized and the amount of feedback required to maximize the throughput is maximized.

One approach (referred to as "Per Antenna Rate Control" or simply PARC) requires: for each MIMO channel, a separate SNR value should be provided as feedback. This approach is not ideal because providing the SNR for each channel requires a large amount of uplink resources. Another approach, known as the "diagonal-bell labs Layered Space-Time Architecture" or simply D-BLAST, requires only a single SNR value as feedback, but for a portion of the MIMO channel requires the transmission of a null signal before transmitting the sequence of encoded data blocks. This results in inefficient use of the channel. The third method, referred to as "Code Reuse Bell labs layered Space Time Architecture" (or CR-BLAST for short), also requires only a single SNR value as feedback, but it encodes all MIMO streams using a single common encoder. As a result, it cannot utilize Successive Interference Cancellation (SIC) and individually optimized rate control. Unless it is combined with highly complex iterative demodulation and decoding, the performance of CR-BLAST is much worse than systems using SIC and individually optimized rate control. It is therefore desirable to provide a system and method having the following advantages, among others: in the uplink, a reduced amount of feedback (e.g., less than if there were a separate SNR for each channel) may be transmitted from the mobile station to the base station; the utilization rate of the channel is not reduced due to the transmission of null signals; as well as separate rate control and SIC may be applied.

Disclosure of Invention

The various embodiments of the invention disclosed herein address one or more of the above needs and provide systems and methods that improve the performance of a MIMO wireless communication system by reducing the amount of uplink resources needed to provide channel performance feedback in order to adjust the data rate in the downlink MIMO channel. In one embodiment, the data streams are conventionally encoded, interleaved, and mapped to modulation symbols in the base station. The modulation symbols are then mixed according to a pseudo-random pattern and transmitted using a set of transmit antennas such that the data in each data stream is transmitted over all of the MIMO channels. In one embodiment, all permutations of the various possible combinations are used. The data is received at the mobile station, where it is de-mixed (de-aligned) and decoded. One SNR is determined for each data stream. In one embodiment, the data stream is decoded with successive interference cancellation. The compressed SNR metrics (such as reference SNR and Δ SNR) are then computed and sent back to the base station. The base station determines SNRs for each data stream based on the compressed SNR metrics and uses the SNRs to adjust the data rates at which the data streams are encoded. In another embodiment, the data stream is decoded without the SIC. In this case, the Δ SNR part in the compressed SNR is set to zero.

One embodiment includes a method comprising: encoding each data stream of a set of data streams according to a corresponding data rate; mixing data streams on a set of MIMO channels according to all permutation modes of various combinations; transmitting the arranged data stream; receiving the arranged data stream; inverting the data streams; for each data stream, decoding and determining the SNR; computing a compressed SNR metric for the set of data streams; providing the compressed metric as feedback; determining a set of individual SNR metrics for the data streams based on the compressed SNR metrics; and adjusting the data rate at which the data streams are encoded based on the individual SNR metrics.

Another embodiment comprises a MIMO wireless communication system. The system comprises: a base station having a plurality of MIMO transmit antennas; and a mobile station having a plurality of MIMO receive antennas. The base station is configured to: encoding each of the plurality of data streams according to a respective data rate; arranging the data streams; and transmitting each of the data streams over a plurality of MIMO channels corresponding to the MIMO transmit antennas. The mobile station is configured to: inverting the data streams to reproduce the encoded data streams; decoding the data streams; and determining a quality metric corresponding to each data stream. The mobile station then determines a compressed quality metric based on the quality metrics corresponding to each data stream and sends the compressed quality metric back to the base station. The base station is configured to: determining a separate quality metric associated with each data stream based on the compressed quality metrics; the data rate at which the data streams are encoded is then adjusted based on the individual quality metrics.

Many alternative embodiments are possible.

Drawings

Fig. 1 is a functional block diagram illustrating the structure of an exemplary wireless transmitter;

FIG. 2 is a functional block diagram illustrating the structure of an exemplary wireless receiver;

fig. 3 is a diagram illustrating transmission of each data stream of a set of data streams over a corresponding set of MIMO channels in accordance with the prior art;

FIGS. 4A and 4B are a pair of diagrams illustrating transmission of each data stream of a set of data streams over each channel of a set of MIMO channels according to one embodiment;

FIG. 5 is a table showing all possible permutations of transmitting four data streams over four MIMO channels;

FIG. 6 is a functional block diagram illustrating the structure of a system using a pseudo-random antenna arrangement and successive interference cancellation according to one embodiment; and

fig. 7 is a flow diagram illustrating a process for processing and transmitting multiple data streams and a process for determining a compressed metric to be provided as feedback to control the data rate in processing the data streams in a MIMO communication system, in accordance with one embodiment.

Detailed Description

One or more embodiments of the present invention are described below. It should be noted that these embodiments, as well as any other embodiments described below, are exemplary and intended to be illustrative of the invention rather than limiting.

As described herein, various embodiments of the present invention include systems and methods for improving the performance of a MIMO wireless communication system by reducing the amount of uplink (reverse link) resources required for providing SNR/channel performance feedback in order to adjust the data rate in the downlink (forward link) MIMO channel.

In one embodiment, a set of data streams in a base station are encoded with corresponding data rates. The encoded data stream is then ready for transmission. However, instead of transmitting each of the encoded data streams over a single MIMO channel, successive blocks in a frame of each encoded data stream are mixed and transmitted over different MIMO channels. I.e. the data streams are arranged on different channels.

In this embodiment, the first block of each data stream is transmitted over a first combination of MIMO channels. For example, if there are four data streams numbered 1-4 and four MIMO channels numbered 1-4, then the first blocks of data streams 1-4 may be transmitted over MIMO channels 1-4, respectively. Then, a second block of data streams 1-4 may be transmitted over MIMO channels 2, 3, 4, and 1, respectively, and a third block may be transmitted over channels 3, 4, 1, and 2, respectively. In this embodiment, the contiguous blocks of data streams 1-4 are transmitted over 24 possible permutations of MIMO channels 1-4.

The multiple MIMO channels transmitted by the base station are spatially distinguishable by the MIMO receiver of the mobile station. Thus, the mobile station can obtain multiple blocks of coded data from each MIMO channel and reconstruct the coded data streams (assuming the mobile station knows the permutation scheme used by the base station to mix the multiple blocks of data streams over the MIMO channel). The receiver then decodes the data and determines the SNR for each data stream.

Because multiple blocks of each data stream have been transmitted over all four MIMO channels, each of the four data streams will generally experience the same channel conditions if the channel remains nearly static during the transmission of the entire encoded frame. As a result, when the SNR (averaged over a frame) is determined for each data stream, the SNR value will only change due to interference cancellation, which can be achieved when each data stream is decoded and then used as feedback to remove the relevant interference from the remaining data streams to be decoded subsequently. This is called successive interference cancellation.

Since the SNRs of the four data streams are changed only by successive interference cancellation, the SNR values do not vary drastically but are relatively smooth. This is true even though the MIMO channel conditions may vary widely (and thus may cause the SNR of the data streams transmitted separately through the respective individual MIMO channels to vary considerably).

The fact that the SNRs for the different data streams are relatively flat allows to express these SNR values in a compressed form, i.e. a more compact form than providing four different SNR values separately, with reasonable accuracy. For example, the SNRs may be represented by a reference SNR value and a Δ SNR value, where the reference SNR value corresponds to the SNR of the first decoded data stream and the Δ SNR value corresponds to the difference between the SNR values of the subsequent data streams.

The mobile station transmits the compressed SNR representation to the base station over an uplink. Since the compressed SNR representation is smaller than the representation of four individual SNR values, less uplink resources are required to provide this feedback to the base station. Next, the base station adjusts the data rate for subsequent encoding of the different data streams based on the compressed representation of the SNRs for the different data streams. In other words, for one data stream, the base station will assume that the SNR measured by the mobile station is equal to the reference SNR value, and will adjust to the data rate for that data stream as indicated by the reference SNR for the next data stream, the base station will assume that the measured SNR value is equal to the reference SNR value plus the Δ SNR value. For the next data stream, a value equal to the reference SNR plus twice the Δ SNR will be used, and so on, with the data rate of each data stream adjusted accordingly.

Before discussing example embodiments in detail, it will be useful to describe the basic operation of a single physical channel in a typical wireless communication system. Referring to fig. 1, a functional block diagram of the structure of an example wireless transmitter is shown.

As shown in fig. 1, an encoder 110 receives and processes a data stream. The data stream is encoded at a selected data rate, as will be further described below. The encoded data stream is forwarded to an interleaver (interleaver)120 and then to a mapper/modulator 130. The modulated signal is then forwarded to the antenna 140, and the antenna 140 transmits the modulated signal.

Referring to fig. 2, a functional block diagram of the structure of an exemplary wireless receiver is shown. In this figure, an antenna 250 receives a signal transmitted by antenna 140 and then forwards the signal to a demodulator/demapper (demapper) 260. Demodulates the signal and passes it to a deinterleaver (deinterleaver) 270. After de-interleaving the signal, it is decoded by a decoder 280 to reproduce the original data stream. It should be noted that errors may occur in the processing of the signal by the transmitter and receiver, so the term "original data stream" as used herein refers to the decoded signal, regardless of whether it is a completely accurate reproduction of the original signal or contains some errors.

Fig. 1 and 2 show mechanisms for transmitting information in a single direction. For example, information may be transmitted from a base station to a mobile station in a cellular telephone system. Typically, communications are bi-directional rather than unidirectional, so similar structural arrangements can be used to transmit information from a mobile station to a base station and vice versa. In such systems, communication from a base station to a mobile station is typically referred to as the forward link, and communication from a mobile station to a base station is referred to as the reverse link.

As described above, in a transmitter, the encoding of a data stream is based on a data rate selected for data transmission. The data rate is in turn selected based on the quality of the received signal. If the quality of the received signal is high, the receiver can decode the higher data rate. Therefore, it is desirable to increase the data rate so that higher throughput can be achieved. If the received signal quality is low, the receiver can only decode the lower data rate. In this case, it is desirable to reduce the data rate so that there are fewer errors in the decoded data.

In order to determine the data rate that should be selected to encode the data stream, the quality of the received signal must first be determined. In some systems, signal quality is determined by measuring the signal-to-noise ratio (SNR) of the signal. At certain SNR levels, the corresponding data rate may be supported. For example, with an acceptable bit error rate, SNR1 may be supported up to data _ rate1, SNR2 may be supported up to data _ rate2, and so on. Thus, these systems measure the SNR of the received signal and send this information back to the transmitter, which in turn determines whether the data rate currently used to encode the transmitted data is acceptable, or too high, or too low. If the data rate is too high or too low, a more suitable data rate may be selected for subsequent encoding.

In such a single channel scenario, it is a relatively straightforward matter to provide the SNR of the received signal as feedback in order to adjust the data rate at which the data is encoded. The SNR information is sufficient for selecting the data rate and does not constitute a particularly large overhead cost. Even if the overhead cost is considered large, it is difficult to reduce this load because the SNR is a single value and this information is necessary to determine the appropriate data rate.

However, some systems do not have only a single channel. For example, a MIMO (multiple input multiple output) system has multiple physical channels. A MIMO transmitter has multiple antennas, each of which may be used to transmit a different one of multiple MIMO channels. Similarly, a MIMO receiver has multiple antennas that are used to distinguish between different physical channels transmitted by the antennas of the transmitter and also to receive these separate physical channels.

In a typical MIMO system, each channel is handled in substantially the same manner as a single channel system. In other words, for each channel, the data streams are encoded, interleaved, mapped/modulated, transmitted over a respective one of the MIMO antennas, received at the receiver, demapped/demodulated, deinterleaved, and decoded at a selected data rate to construct the original data streams. This process is performed in parallel for each MIMO channel.

The MIMO system is configured such that the physical channels are independent of each other. Thus, multiple data streams may be transmitted separately over different channels. In other words, each data stream may be transmitted over a different transmit antenna and may be distinguished by a multi-antenna MIMO receiver. This is shown in figure 3.

Referring to fig. 3, a diagram illustrating transmission of each data stream of a set of data streams over a corresponding set of MIMO channels according to the prior art is shown. For example, the system of FIG. 3 is intended to represent a PARC system. In the system, a set of encoded data streams 311 and 314 are transmitted via a set of transmit antennas 321 and 324. The transmitted signals are received by receive antennas 331-334. The space-time signal processor 335 processes the received signals (all of which are received by antennas 331-334) to distinguish between data streams 341-344 (which are substantially the same as data streams 311-314).

Because the MIMO channels are independent of each other, different channels may have different attenuation characteristics. In other words, each channel in a MIMO system may have a different SNR. As a result, different channels may need to encode the various data streams at different data rates in order to maximize the throughput of each channel.

A straightforward way to provide such SNR information is: the SNR is measured separately for each MIMO channel and the SNR values are then transmitted back to the transmitter so that the data rate for each channel can be selected based on the respective measured SNR values. This is the method used in PARC systems. Although this approach is straightforward, it also requires a relatively large amount of reverse link resources. If there are n MIMO channels, the method requires n times more resources than the single channel case. Because of the high resource costs associated with this approach, the system and method of the present invention uses an alternative approach that allows the compressed SNR metric to be returned as feedback to the transmitter and thereby saves reverse link resources, while allowing the selection of data rates that will allow the system throughput to be closer to the maximum.

Because the different MIMO channels are independent of each other, they have independent fading characteristics and channel quality. Therefore, the SNRs of these channels are also independent of each other. Because these SNRs are independent, they may vary widely from one another. For example, if there are four channels, a first channel may have an SNR of [ +15] dB, a second channel may have an SNR of [ -15] dB, a third channel may have an SNR of 0dB, and a fourth channel may have an SNR of [ +15] dB. It is clear that in this case it is difficult to characterize the SNR of all channels in compressed form. Thus, embodiments of the present invention use a method that ensures that these SNRs are sufficiently smooth to be expressed in compressed form with reasonable accuracy.

The method used by the embodiments of the present invention involves transmitting data for each data stream over all MIMO channels. In other words, for each data stream, the data is processed in the transmitter in substantially the same manner as a typical MIMO system, but rather than transmitting the data through a single MIMO antenna, one block is transmitted through a first antenna, the next block is transmitted through a second antenna, and so on. The multiple blocks of each data stream are thus spread over all MIMO channels (each MIMO channel being associated with a respective one of the MIMO antennas). This is shown in fig. 4A and 4B.

Referring to fig. 4A, a diagram illustrating transmission of each data stream of a set of data streams over each channel of a set of MIMO channels is shown, in accordance with one embodiment. On the right side of fig. 4A, four data streams 411-414 are shown. Data streams 411-414 correspond to decoded, interleaved, mapped/modulated data, respectively, that has been processed by the transmitter and is ready to be transmitted over the wireless link to the receiver. In particular, the multiple data streams represent data that would conventionally be transmitted over separate channels (i.e., antennas of a MIMO transmitter) in a MIMO system. Within each data stream, there is a series of data blocks. A data block is identified collectively by a letter corresponding to the data stream and a number corresponding to the location of the data block in the data stream. These data blocks may be of any size that is convenient for a particular implementation, but they should not be so large that the benefits of arranging multiple data streams over different channels are not seen.

After the data streams undergo conventional pre-transmit processing, multiple blocks for each data stream are mapped to different antennas in the MIMO transmitter. As shown in fig. 4A, a plurality of blocks of the first group, i.e., a1, B1, C1, and D1 are mapped to antennas 431, 432, 433, and 434, respectively. The next group of multiple blocks a2, B2, C2, and D2 map to different combinations of the four antennas. Specifically, they are mapped to antennas 432, 433, 434, and 431, respectively. In other words, multiple blocks of different data streams have rotated by one block with respect to the antennas. The third set of data blocks is rotated by one block such that data blocks a3, B3, C3, and D3 are mapped to antennas 433, 434, 431, and 432, respectively. Subsequent blocks are similarly mapped to different combinations of these antennas and the range of possible combinations is exhausted. In one embodiment, the series of mappings of data blocks to MIMO channels includes a pseudo-random pattern (as shown and described in connection with fig. 5).

Referring to fig. 4B, a diagram of a reception scenario of a mixed data stream that has been transmitted at a receiver is shown. It can be seen that receive antennas 441-444 receive the combined signals transmitted by transmitter antennas 431-434. The space-time signal processor 445 processes the received signal to distinguish the arranged data streams 451 and 454. The receiver knows the algorithms and/or modes with which the original data streams 411-414 are mapped to the mixed data streams 421-424. Thus, the receiver may perform de-mapping or de-mixing processing on the received data blocks (451- > 454) to reconstruct the original data stream (461- > 464). The reconstructed original data stream 461 & 464 may then be processed by conventional methods for demapping/demodulation, deinterleaving, and decoding.

As can be seen from fig. 4A and 4B, the reconstructed data stream comprises data blocks that have been transmitted in a pseudo-random pattern over all MIMO channels. For example, the reconstructed data stream 411 includes data blocks a1, a2, A3, and so on. The data blocks are transmitted over a first, second, third, etc. MIMO channel. The other reconstructed data streams are also transmitted over all MIMO channels. By transmitting each data stream through all of the MIMO channels, each data stream experiences the same channel conditions on average. In other words, each data stream transmits approximately one-quarter of its data blocks over one MIMO channel, and thus experiences the channel conditions of each MIMO channel for one-quarter of the total time.

In view of the above example of the SNR variation from [ +15] dB to [ -15] dB for different channels, transmitting each data stream over all four channels will result in an average SNR between [ +15] dB to [ -15] dB. For example, the SNR may be [ +5] dB. Although the SNRs for the different data streams are most likely not exactly the same, they should be approximately equal and must be very smooth compared to the SNR variation in a typical MIMO system.

It should be noted that transmitting each data stream over all MIMO physical channels may have additional benefits beyond those obtained by equalizing the SNRs associated with the different data streams. For example, a benefit of transmitting data streams with different signal paths is that such diversity provides a more robust channel.

If each data stream is to be transmitted over multiple physical channels, it is necessary to determine how the different data streams are mixed over the multiple channels. In other words, it is necessary to determine which data stream is transmitted through which antenna at any particular time. In some embodiments, the multiple data streams may simply be rotated over different antennas. For example, if there are four channels, consecutive blocks of a data stream may be transmitted via antennas 1, 2, 3, 4, etc.

While there are many benefits to using such a simple rotation, it is expected that better performance will likely be achieved if a pseudo-random pattern is used that includes a full permutation of the various possible combinations of data streams and physical channels, in terms of equalization of the SNRs associated with the data streams and diversity, among other benefits. The "full" permutation of the various combinations herein refers to all possible orders of the various combinations of data streams and physical channels. Fig. 5 shows an example.

Referring to fig. 5, a table of all possible permutations of transmitting four data streams over four MIMO channels is shown. The data blocks corresponding to a particular data stream are identified with the same letter. For example, all data blocks from the first data stream are identified by the letter a. The data blocks of the second, third and fourth data streams are identified by the letters B, C, D, respectively. Each row of the table corresponds to a particular MIMO channel. Each column of the table corresponds to a contiguous block of data transmitted over the MIMO channel.

It can be seen that at each point in time (i.e. in each column of the table) one data block is transmitted in each of the four data streams. In the first column (left-most), data blocks from data streams A, B, C and D are transmitted on MIMO channels 1, 2, 3, and 4, respectively. In the next column, the data streams (or MIMO channels) are rotated so that data blocks from data streams A, B, C and D are transmitted on MIMO channels 2, 3, 4, and 1, respectively. So that the rotation is performed for a plurality of times.

In the fifth column, the original order of data streams will be turned back to the original combination of data streams and MIMO channels (i.e., data streams A, B, C and D on MIMO channels 1, 2, 3, and 4, respectively). This combination is not repeated but the data streams are arranged such that data streams A, B, C and D are transmitted on MIMO channels 1, 2, 3, and 4, respectively. The data streams are then rotated around in this order until blocks from each data stream are again transmitted on each MIMO channel.

The above process is repeated for each permutation of various combinations of data streams and MIMO channels. The four data streams may be ordered in six different permutations: A-B-C-D; A-B-D-C; A-C-B-D; A-C-D-B; A-D-B-C and A-D-C-B. Each of the ordering of the data streams may then be rotated over four different MIMO channels. For example, A-B-C-D may transmit on channel 1-2-3-4, 4-1-2-3, 3-4-1-2, or 2-3-4-1. As a result, there are 24 different combinations total of four data streams and four MIMO channels (i.e., 4 factorial or 4 |). The use of all of these different combinations to transmit data streams over a MIMO channel is believed to group the various objects of the present invention into a full permutation of the various combinations.

It should be noted that the system described herein is intended to illustrate that alternative embodiments may have different numbers of data streams and/or MIMO channels. For embodiments in which the number of data streams is equal to the number of MIMO channels, the number of different combinations of data streams and MIMO channels is defined by n! (n factorial) where n is the number of data streams/MIMO channels. Thus, for example, a system with three data streams and three MIMO channels would have a 3! I.e. 6 different combinations of full permutations. A system with five data streams and five MIMO channels would have a 5! I.e. 120 different combinations of full permutations.

The SNRs for the different data streams are smoothed because multiple blocks of each data stream have been transmitted over all of the MIMO channels and experience substantially the same channel conditions. Ideally, the SNRs for these data streams are equal. Thus, it is possible to provide feedback to the transmitter in the form of a single SNR representing all data streams. However, this may not provide the highest throughput for these data streams.

In one embodiment, the MIMO receiver is a linear receiver without nonlinear interference cancellation.

By applying the above-described pseudo-random antenna arrangement, the highest data rate can be achieved with only a single SNR feedback if there is no successive interference cancellation operation at the receiver. When the received vector of the nxn MIMO system at symbol time k is represented by y (k) such that there is the following formula (1),

SNR of the ith stream becomes equal in a linear Minimum Mean Square Error (MMSE) receiver

Wherein the ith noise covariance matrix is expressed as

In the above-mentioned (1) to (3),representing the channel matrix, x_Nx1(k)＝[x⁽¹⁾(k)，x⁽²⁾(k)，…，x^(N)(k)]^TRepresents a normalized signal vector, and n_N×l(k) Representing the background noise vector received by N receive antennas whose variance is σ²Dimension/dimension. Although the MIMO system considered here has N data streams, N transmit antennas, and N receive antennas, the number of MIMO transmit streams need not equal the number of transmit antennas nor the number of receive antennas. Also, the number of transmit antennas and the number of receive antennas need not be equal.

In general, different streams will see different SNR values because there will be different received channel vectors for different transmit antennas. When the number of symbols in the coded block and system bandwidth is denoted by K and W, respectively, the achievable data rate (bits/sec) for the ith stream of the PARC system can be calculated in the quasi-static channel by using the following mapping (or by using any other well-designed SNR-rate mapping formula):

i＝1，2，...，N. (4)

it should be noted that the time index k is deliberately left out when expressing the SNR, since a quasi-static channel is assumed. The N requested data rates are fed back and used to encode the next N-stream data frame. The total data rate that can be achieved by independent per-stream coding is given by:

now, if a pseudo-random antenna permutation is applied as in fig. 3-4, it can be seen that the rates of the N streams will have the same value. More specifically, when the permuted antenna index of the ith stream is denoted as π (i, k) at time k, the achievable data rate for the ith stream is:

i＝1，2，...，N， (6)

and all R⁽ⁱ⁾All having the same value. If the encoded frame size is large and random-like encoding is used (e.g., turbo encoding), the total achievable data rate is still given by (5). The relationship between PARC and pseudorandom antenna arrangement is similar even when linear Zero Forcing (ZF) or matched-filtered (MF) receivers are assumed instead of MMSE receivers. It should be noted that in order to achieve maximum data rates in the linear receiver case, only antenna cycling and a single SNR feedback are required, and not all permutations need to be employed.

In one embodiment, a MIMO receiver uses Successive Interference Cancellation (SIC) in decoding the data streams. The SIC receiver achieves improved SNR values for certain data streams by first decoding one of the data streams and then using this information to cancel some of the interference in the remaining data streams. More specifically, the first decoded data stream is used to reproduce interference generated during transmission. This interference can then be cancelled from the received data stream superposition. The second data stream is then decoded. The SNR of the second decoded data stream is greater than the SNR of the first decoded data stream because the interference cancellation in the first data stream has reduced the interference in the second data stream. The second decoded data stream is then used to cancel some of the interference in the remaining data stream in the same manner as the first data stream. The above process is repeated for each remaining data stream.

When using the SIC method, the SNR associated with a particular data stream corresponds to the order in which the data streams are decoded, with the first data stream to be decoded having the lowest SNR and the last data stream to be decoded having the highest SNR. Because the SNRs for different data streams are not the same, the data streams may support different data rates (i.e., be encoded at different data rates). The data stream with the lowest SNR supports the lowest data rate, while the data stream with the highest SNR supports the highest data rate. If a single SNR value is provided as feedback by the receiver and the transmitter selects a data rate based on this feedback to encode each data stream, the maximum possible throughput will not be achieved for the data streams with the higher SNRs. Thus, in the present embodiment, it is useful to provide some indication of the difference between the SNRs for different data streams so that an appropriate data rate can be selected for each data stream.

When MMSE-SIC or ZF-SIC decoders are used at the receiver, the maximum data rate cannot be achieved in a strict sense unless N SNR values are provided as feedback. However, as described herein, by applying a suitable approximation formula, it is possible to achieve close to the maximum data rate with a compressed SNR (i.e., reduced feedback) in a practical sense.

On the other hand, when the MF-SIC decoder is aligned with and used with pseudo-random antennas, the SNR values for the other data streams can be more accurately calculated at the transmitter by using the SNR of the first data stream and the average channel correlation factor in these streams. At the output of the MF (or pilot-weighted combiner), the instantaneous SNR of the first stream can be expressed as:

p, N and σ therein²Respectively representing the signal energy, the number of data streams and the variance of the background noise. A simple way to calculate the average SNR of a coded frame, although it is not optimal in terms of achievable data rate, is to take the average signal power (more specifically, the arithmetic mean) and the average (arithmetic mean) interference-to-noise filtering ratio such that:

wherein the average channel correlation factor is calculated according to the following formula:

likewise, the average SNR of the encoded frames of the ith stream, which was decoded after the cancellation of the first i-1 streams, can be calculated. Due to the symmetrical structure of the pseudo-random antenna arrangement, the difference in the effective number of interfering signals is used to achieve a similar SNR result as the 1 st stream, expressed as:

from (8) and (10), the SNR relationship between the 1 st stream and the ith stream can be derived, so it is:

or equivalently, the SNR relationship can be rewritten as following (12) until the SNR of the last frame.

Thus, if the SNR of the first decoded stream (or the last or any other decoded stream) and the average channel correlation factor are available, the SNR values of the other streams of the pseudo-random antenna permutation system used with the MF-SIC receiver can be accurately predicted. Equations (11) - (12), however, present only one example of how the full set of SNR values for all data streams can be recovered when only one SNR value and one associated parameter is available. It should be noted that a more sophisticated and efficient SNR based on equation (6) should be provided as feedback, rather than an arithmetic average based average SNR based on equation (10), so that a more appropriate and more optimal rate selection can be made. Thus, in practical implementations, any other formula that can effectively interpret the SNR relationships for multiple streams in a given MIMO system can be used with the reference SNR and one or a series of auxiliary parameters. The auxiliary parameter may be an average channel correlation factor, Δ SNR, or other.

(11) The SNR prediction formula in (12), which is an accurate calculator of SNR values in the case of MF-SIC receivers, can be used as the lower SNR bound for MMSE-SIC receivers. In fact, if the background noise is blank, the SNR of the last decoded stream will be the same value between MF-SIC and MMSE-SIC, and the SNR gaps between the other streams (i.e., MMSE SNR-MF SNR) will be highly dependent on the average channel correlation factor. When the average channel correlation factor is small (or most spatial symbol differences (spatial signatures) are close to orthogonal to each other), the gap will be close to zero even for other streams (and the SNR values on different streams will be almost the same); otherwise it may become too large. Assuming that the MS returns the SNR of the last decoded stream and the average channel correlation factor of equation (9), the base station can carefully select the rate based on equation (12) so that the following streams can be decoded almost certainly once the first stream is decoded. On the other hand, the base station can reduce the reported average channel correlation factor to a smaller value in consideration of the capability of the advanced receiver (i.e., MMSE-SIC): if it is large, then the average channel correlation factor reported in equation (9) is reduced to a small value; and if it is small it is left almost unchanged.

Alternatively, the mobile station in the decoding stage can actually generate all the average SNR values for the N streams and estimate the best effective average channel correlation factor so that the curve in equation (12) (or another curve appropriately designed for MMSE-SIC or ZF-SIC) is as close as possible to the generated SNR values. The SNR of the last stream and the effective average channel correlation factor are then fed back so that the base station can select the rate according to equation (12).

In practice, it is possible to derive a better approximation of the SNR relation than equation (12) in MMSE-SIC or ZF-SIC receivers, in terms of a simple and efficient description of the SNR relation. For example, it is possible to target appropriately selected auxiliary parameters ρ and recursive functions f⁽ⁱ⁾Using an additional SNR relation

Or multiplicative SNR relation

For simple implementations, the recursive function may take on a constant value, e.g.,

in one embodiment, the feedback provided by the receiver includes a reference SNR value and a Δ SNR value. Because the channel quality experienced by each data stream is substantially the same, the difference in SNR for each data stream results from the cancellation of interference when decoding successive data streams. Since the effect of SIC on the SNR of successive data streams is smooth and well understood, the SNR of these data streams can be properly estimated with a reference SNR value, which is the actual SNR value of the first decoded channel (or the last channel, or any other pre-specified channel, depending on the system design), and a Δ SNR value, which is the amount by which the SNR of each subsequently decoded channel is increased (or decreased, depending on the system design). For example, the SNR of the first decoded channel is equal to the reference SNR, the SNR of the second decoded channel is equal to the reference SNR plus Δ SNR, the SNR of the third decoded channel is equal to the reference SNR plus 2 times Δ SNR, and so on. It should be noted that it is assumed that the base station knows the order in which the mobile stations decode the data streams and is therefore able to apply these SNRs (the reference SNR plus a multiple of the Δ SNR) to the appropriate data streams. The above calculations and Δ SNR addition operations may be performed in a linear scale, or in a decibel (dB) scale. Since the addition operation in the dB scale corresponds to the multiplication operation in the linear scale, the addition operation in the linear scale and the addition operation in the dB scale are equivalent to the use of (13) and (14), respectively, where(linear-scale-value).

Referring to fig. 6, a functional block diagram of the structure of a system that employs a pseudo-random antenna arrangement and successive interference cancellation according to one embodiment is shown. In this embodiment, the system includes a transmitter 610 and a receiver 620. In one embodiment, transmitter 610 is implemented in a wireless base station and receiver 620 is implemented in a wireless mobile station, forming a communication downlink. The mobile station also includes a transmitter and the base station also includes a receiver, thereby forming a communication uplink.

Transmitter 610 and receiver 620 are both MIMO devices configured to transmit and receive four channels. The transmitter 610 is configured to process four data streams and transmit respective encoded data streams over a pseudo-random combination of four physical MIMO channels. Receiver 620 is configured to receive data on the four MIMO channels, reconstruct the encoded data streams, and process the data to regenerate the original data streams.

Referring to transmitter 610, four original data streams are received by encoder 630. Each encoder encodes a respective data stream at a data rate selected for that particular data stream. The encoded data symbols are then interleaved by interleaver 635 and mapped to modulation symbols by mapper 640. Permutation unit 645 then maps the modulation symbols to antennas 650. Antenna 650 then transmits the modulation symbols according to the permutation scheme implemented by permutation unit 645.

Referring to the receiver 620, the transmitted symbols are received by the antenna 655 and forwarded to the first equalizer 660. The first equalizer calculates the SNR of the first data stream and forwards the signal to the first demapper 665. The encoded symbols are then deinterleaved by a first deinterleaver 670 and decoded by a first decoder 675. The decoded data is provided to a first interference canceller 680 that regenerates interference corresponding to the first data stream and removes such interference from the received signal. Similar processing paths are provided for signals corresponding to the remaining data streams.

After all four data streams have been decoded, the SNR has been determined for each data stream. As described above, by transmitting these data streams through all MIMO channels, their SNRs become equalized, so the difference in SNR determined for each data stream is caused by successive interference cancellation. Thus, the receiver can compute a compressed SNR metric for a smooth set of SNRs for the four data streams. In one embodiment, the compressed metric includes a reference SNR value and a Δ SNR value, where the Δ SNR value is the difference in SNR of the continuous data stream in a linear scale or dB scale. The compressed metric is then provided as feedback to a transmitter, which can adjust the data rates at which the different data streams are encoded based on the corresponding SNRs, as determined from the compressed SNR metric.

Fig. 7 summarizes the operation of the present system. Fig. 7 is a flow diagram illustrating a process for processing and transmitting multiple data streams and a process for determining a compressed metric to be provided as feedback to control the data rate in processing the data streams in a MIMO communication system, in accordance with one embodiment.

As shown in fig. 7, a set of n initial data streams is first processed to produce a corresponding set of encoded data streams (700). This process corresponds to the encoding, interleaving, and mapping/modulation operations performed on the entire data frame by the various components 630, 635, and 640 of the transmitter 610. Successive portions (e.g., blocks) of a frame of each encoded data stream are transmitted in turn over multiple MIMO channels (705). As described above, for example, the rotating transmissions on the multiple MIMO channels may follow a pseudo-random pattern. In one embodiment, the pseudo-random pattern includes all possible permutations of various combinations of data streams and MIMO channels. The mixing and transmitting operations of the encoded data streams correspond to components 645 and 650 of transmitter 610.

The transmitted data is then received by the receiver (710). The receiver is a MIMO receiver that is capable of spatially distinguishing between different MIMO channels. Unmixing operations are performed on the mixed portions of the data stream and the encoded data stream is reconstructed (715). After the encoded data streams are reconstructed, the SNR is determined for each of the encoded data streams and the encoded data streams are decoded into the original data stream (720, 725). As described above, in the embodiment of fig. 6, the data streams are sequentially decoded and used to reproduce interference corresponding to the decoded data streams, and then the interference is removed.

When a good SNR has been determined for each data stream, a compressed SNR metric is calculated from these values (730). As described above, in one embodiment, the compressed metric includes a reference SNR value and a Δ SNR value. The compressed SNR value is then sent back to the transmitter (735). As described above, the transmitter 610 and the receiver 620 form a downlink of the wireless communication system, which further includes an uplink transmitter and receiver (not shown in fig. 6) for transmitting the compressed SNR metric as feedback. Upon receiving the compressed SNR metrics, the SNR for each data stream is reconstructed 740 and the data rate at which each data stream is encoded is adjusted 745 based on the SNR values. If the receiver does not use successive interference cancellation, Δ SNR is set to 0 in the linear scale and 0dB in the dB scale.

In one embodiment, the receiver may also feed back some information for requesting to turn off certain transmit antennas. The pseudo-random antenna arrangement presented and the compressed SNR feedback will then be applied only on the active transmit antenna that is actually transmitting the data stream.

In another embodiment, the number of data streams currently in use (N)_s) May be less than the number of transmit antennas (N)_t)。N_t-N_sA single transmit antenna may not transmit any signal at a given time. Even in this case, it can be said that N still remains_t-N_sIndividual data streams, except that they have zero transmit power, so that a pseudo-random antenna arrangement and compressed SNR feedback can be applied.

As described above, the above embodiments are illustrative of the present invention and not restrictive. Various alternative embodiments may have many variations in comparison to the systems and methods described herein. For example, alternative embodiments may use a compressed feedback metric that is constructed using a value other than the reference SNR value and the Δ SNR value. In fact, the metric may comprise a number of values other than SNR, such as the error rate in the received, decoded data stream. Alternative embodiments may also have different types of receivers (such as non-SIC types), different numbers of channels, and other changes.

Although not discussed in detail above, it should be noted that the above-described functionality can be implemented in the mobile stations and base stations of the wireless communication system by providing suitable programs that are executed in the processing subsystems of these devices. These processing subsystems then control the processing of data and the transmission/reception of data by the various transceiver subsystems of the mobile station and the base station.

The program instructions are typically embodied in a storage medium that is readable by the various processing subsystems. Example storage media may include RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage media known in the art. Such storage media containing program instructions for implementing the functions described above include alternative embodiments of the present invention.

Those of skill in the art would understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and method steps described in connection with the embodiments disclosed above may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. It should be noted that the illustrative components, blocks, modules, circuits, and steps may be reordered or otherwise reconfigured in alternative embodiments. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with various means: a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof for performing the various functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (46)

1. A method implemented in a multiple-input multiple-output (MIMO) wireless communication system, comprising:

transmitting each of a plurality of data streams from a first station to a second station in a permuted manner through a plurality of MIMO channels;

performing an inverse permutation operation on the data streams at the second site;

determining a quality metric for each data stream at the second site;

determining, at the second station, a compressed quality metric based on the quality metrics for the data streams; and

the quality metric of the compression is transmitted from the second station to the first station.

2. The method of claim 1, further comprising:

encoding, at a first station, each of a plurality of data streams according to a respective data rate; and

the data rate at which the data streams are encoded is adjusted at the first station based on the compressed quality metric.

3. The method of claim 2, wherein the quality metric comprises a signal-to-noise ratio (SNR), wherein the compressed quality metric comprises a reference SNR value and a Δ SNR value, and wherein adjusting the data rate at which the data streams are encoded based on the compressed quality metric comprises: the data rate for each channel is adjusted based on the reference SNR value plus a multiple of the Δ SNR value.

4. The method of claim 1, wherein the first site is a base station and the second site is a mobile station.

5. The method of claim 1, wherein transmitting the data stream in a permuted manner comprises: the data streams are mixed in a pseudo-random pattern over the MIMO channels.

6. The method of claim 5, wherein the pseudo-random pattern comprises a full permutation of various possible combinations of data streams and MIMO channels.

7. The method of claim 1, wherein the quality metric comprises a signal-to-noise ratio (SNR).

8. The method of claim 7, wherein the quality metric of the compression comprises a reference SNR value and a Δ SNR value.

9. The method of claim 1, further comprising: the encoded data streams are decoded at the second site.

10. The method of claim 9, wherein the encoded data streams are decoded at the second site using successive interference cancellation.

11. A method for multiple-input multiple-output (MIMO) wireless communication, comprising:

encoding each of the plurality of data streams according to a respective data rate;

transmitting the data streams to the second station in a permuted manner over a plurality of MIMO channels;

receiving a compressed quality metric; and

the data rate at which the data streams are encoded is adjusted based on the quality metric of the compression.

12. The method of claim 11, wherein transmitting the data streams in an arranged manner comprises: the data streams are mixed in a pseudo-random pattern over multiple MIMO channels.

13. The method of claim 12, wherein the pseudo-random pattern comprises a full permutation of various possible combinations of data streams and MIMO channels.

14. The method of claim 11, wherein the quality metric comprises a signal-to-noise ratio (SNR).

15. The method of claim 14, wherein the quality metric of the compression comprises a reference SNR value and a Δ SNR value.

16. The method of claim 15, wherein adjusting the data rate at which the data streams are encoded based on the quality metric of the compression comprises: the data rate for each channel is adjusted based on the reference SNR value plus a multiple of the Δ SNR value.

17. A method for multiple-input multiple-output (MIMO) wireless communication, comprising:

receiving a plurality of arranged data streams over a plurality of MIMO channels;

inverting the data streams;

determining a quality metric for each data stream;

determining a compressed quality metric based on the quality metrics for the data streams; and

the compressed quality metric is transmitted to a base station.

18. The method of claim 17, wherein the data streams are arranged in a pseudo-random pattern.

19. The method of claim 18, wherein the pseudo-random pattern comprises a full permutation of various possible combinations of data streams and MIMO channels.

20. The method of claim 17, wherein the quality metric comprises a signal-to-noise ratio (SNR).

21. The method of claim 20, wherein the quality metric of the compression comprises a reference SNR value and a Δ SNR value.

22. The method of claim 17, further comprising: these encoded data streams are decoded.

23. The method of claim 22, wherein the encoded data streams are decoded using successive interference cancellation.

24. A base station for a MIMO wireless communication system, comprising:

a processing subsystem; and

a transceiver subsystem having a plurality of transmit antennas and coupled to the processing subsystem;

wherein the processing subsystem is configured to

Each of the plurality of data streams is encoded according to a respective data rate,

rank the data streams and control the transceiver subsystem to transmit each of the data streams over a plurality of MIMO channels corresponding to transmit antennas, receive compressed quality metrics associated with all of the data streams,

determining a separate quality metric associated with each data stream based on the compressed quality metrics, an

The data rate at which each data stream is encoded is adjusted based on a separate quality metric associated with the data stream.

25. The base station of claim 24, wherein the processing subsystem is configured to mix the data streams in a pseudo-random pattern over MIMO channels.

26. The base station of claim 25, wherein the pseudo-random pattern comprises a full permutation of various possible combinations of data streams and MIMO channels.

27. The base station of claim 24, wherein the quality metric comprises a signal-to-noise ratio (SNR).

28. The base station of claim 27, wherein the quality metrics for the compression comprise a reference SNR value and a Δ SNR value.

29. The base station of claim 28, wherein the processing subsystem is configured to: the data rate at which each data stream is encoded is adjusted by calculating a corresponding SNR equal to the reference SNR value plus a multiple of the Δ SNR value, where successive data streams have successively higher and higher SNRs.

30. A mobile station for a MIMO wireless communication system, comprising:

a processing subsystem; and

a transceiver subsystem having a plurality of receive antennas and coupled to the processing subsystem; wherein the processing subsystem is configured to

Receiving the permuted data streams through the receive antennas,

the data streams are subject to an inverse permutation,

the data streams are decoded and the data stream is decoded,

a separate quality metric is determined corresponding to each data stream,

determining a compressed quality metric based on the individual quality metrics corresponding to each data stream, an

Controlling the transceiver subsystem to transmit the compressed quality metric to a base station.

31. The mobile station of claim 30, wherein the processing subsystem is configured to: these data streams are arranged inversely according to a pseudo-random pattern.

32. The mobile station of claim 31, wherein the pseudo-random pattern comprises a full permutation of various possible combinations of data streams and MIMO channels.

33. The mobile station of claim 30, wherein the quality metric comprises a signal-to-noise ratio (SNR).

34. The mobile station of claim 33, wherein the quality metrics of the compression comprise a reference SNR value and a Δ SNR value.

35. The mobile station of claim 30, further comprising: a processing subsystem for decoding the encoded data streams.

36. The mobile station of claim 35, wherein the processing subsystem decodes the encoded data streams using successive interference cancellation.

37. A multiple-input multiple-output (MIMO) wireless communication system, comprising:

means for transmitting each of a plurality of data streams from a first station to a second station in an arranged manner over a plurality of MIMO channels;

means for inverting the data streams at the second site;

means for determining a quality metric for each data stream at the second site;

means for determining, at the second station, a compressed quality metric based on the quality metrics for the data streams; and

means for transmitting the quality metric of the compression from the second station to the first station.

38. The system of claim 37, further comprising:

means for encoding, in the first station, each of the plurality of data streams according to a respective data rate; and

means for adjusting, at the first station, a data rate at which the data streams are encoded based on the compressed quality metric.

39. The system of claim 38, wherein the quality metric comprises a signal-to-noise ratio (SNR), wherein the compressed quality metric comprises a reference SNR value and a Δ SNR value, and wherein the means for adjusting the data rate is configured to adjust the data rate for each channel based on the reference SNR value plus a multiple of the Δ SNR value.

40. The system of claim 37, wherein the first site is a base station and the second site is a mobile station.

41. The system of claim 37, wherein means for transmitting the data streams in a permuted manner is configured to mix the data streams in a pseudo-random pattern over MIMO channels.

42. The system of claim 41, wherein the pseudo-random pattern comprises a full permutation of various possible combinations of data streams and MIMO channels.

43. The system of claim 37, wherein the quality metric comprises a signal-to-noise ratio (SNR).

44. The system of claim 43, wherein the quality metric of the compression comprises a reference SNR value and a Δ SNR value.

45. The system of claim 37, further comprising: means for decoding the encoded data streams at the second site.

46. The system of claim 45, wherein the means for decoding the encoded data streams is configured to: these encoded data streams are decoded with successive interference cancellation.