CN115580328B

CN115580328B - A beamforming method, device, storage medium and program product

Info

Publication number: CN115580328B
Application number: CN202110688884.4A
Authority: CN
Inventors: 李健之; 朱理辰; 郑占旗
Original assignee: Datang Mobile Communications Equipment Co Ltd
Current assignee: Datang Mobile Communications Equipment Co Ltd
Priority date: 2021-06-21
Filing date: 2021-06-21
Publication date: 2025-02-11
Anticipated expiration: 2041-06-21
Also published as: CN115580328A

Abstract

The application provides a beam forming method, a beam forming device, a storage medium and a program product. The method comprises the steps of receiving uplink reference signals, determining an autocorrelation matrix of channel estimation corresponding to current beamforming according to the uplink reference signals, determining a channel eigenvector of current beamforming according to the autocorrelation matrix corresponding to current beamforming and a channel eigenvector of previous beamforming, and performing beamforming on downlink signals according to the channel eigenvector of current beamforming. Thus, the beamforming efficiency is improved.

Description

Beam forming method, device, storage medium and program product

Technical Field

The present application relates to the field of communications technologies, and in particular, to a beamforming method, a device, a storage medium, and a program product.

Background

The wave beam forming is to concentrate the signal power in a certain direction by adjusting the weight vector of each antenna in the antenna array, reduce the space interference and improve the link quality.

In non-codebook beamforming, beamforming (Eigenvalue Based Beamforming, EBB) based on eigenvalues is generally used, the beamforming method needs to continuously perform eigenvalue decomposition (Eigenvalue Decomposition, EVD) on a channel, specifically, after obtaining channel estimation of an uplink Sounding reference signal (Sounding REFERENCE SIGNAL, SRS), a base station performs EVD operation, and determines beamforming weights according to channel eigenvectors obtained by the EVD operation, so as to perform beamforming processing on the signal according to the beamforming weights. In practical applications, when the channel feature vector is obtained through EVD operation, the exponentiation method is generally adopted to start iteration with a random non-zero initial vector to obtain the channel feature vector, and this method is low in efficiency.

Disclosure of Invention

The application provides a beam forming method, a beam forming device, a storage medium and a program product, so as to improve the beam forming efficiency.

In a first aspect, the present application provides a beamforming method, including:

Receiving an uplink reference signal, and determining an autocorrelation matrix of channel estimation corresponding to the current beamforming according to the uplink reference signal;

Determining the channel eigenvector of the current beamforming according to the autocorrelation matrix corresponding to the current beamforming and the channel eigenvector of the previous beamforming;

And carrying out beam forming on the downlink signal according to the channel characteristic vector of the current beam forming.

In one embodiment, the determining the channel eigenvector of the current beamforming according to the autocorrelation matrix corresponding to the current beamforming and the channel eigenvector of the previous beamforming includes:

Aiming at the ith channel characteristic vector of the current beamforming, taking the ith channel characteristic vector of the previous beamforming as an initial vector, wherein the value of i traverses all integers from 1 to n, n is the number of streams of the current beamforming, and n is a positive integer;

The vector obtained by multiplying the initial vector by the autocorrelation matrix corresponding to the current beamforming is used as a new initial vector, the steps are repeatedly executed until the first preset times are reached, and the obtained vector is determined to be the ith channel characteristic vector of the current beamforming;

Determining an ith channel eigenvalue of the current beamforming according to an ith channel eigenvector of the current beamforming and the autocorrelation matrix corresponding to the current beamforming, and updating the autocorrelation matrix corresponding to the current beamforming according to the ith channel eigenvalue of the current beamforming and the ith channel eigenvector of the current beamforming, wherein the updated autocorrelation matrix is used for determining an (i+1) th channel eigenvector of the current beamforming;

and executing the steps to determine the channel characteristic vector of the current beam forming.

And determining the channel eigenvector of the current beamforming according to the autocorrelation matrix corresponding to the current beamforming, and the channel eigenvector and the channel eigenvalue of the previous beamforming.

In one embodiment, the determining the channel eigenvector of the current beamforming according to the autocorrelation matrix corresponding to the current beamforming and the channel eigenvector and the channel eigenvalue of the previous beamforming includes:

Aiming at the ith channel characteristic vector of the current beamforming, taking the ith channel characteristic vector of the previous beamforming as an initial vector, and taking the ith channel characteristic value of the previous beamforming as an initial characteristic value, wherein the value of i traverses all integers from 1 to n, n is the number of streams of the current beamforming, and n is a positive integer;

Determining an inverse matrix according to the autocorrelation matrix and the initial eigenvalue corresponding to the current beamforming;

The vector obtained by multiplying the initial vector by the inverse matrix is used as a new initial vector, the step is repeatedly executed until the second preset times are reached, the obtained vector is determined to be the ith channel characteristic vector of the current beamforming, and the ith channel characteristic value of the current beamforming is determined according to the ith channel characteristic vector of the current beamforming and the autocorrelation matrix corresponding to the current beamforming;

Determining a difference matrix of the autocorrelation matrix of the channel estimation corresponding to the current beamforming and the autocorrelation matrix of the channel estimation corresponding to the previous beamforming;

and under the condition that the 2-norm of the difference matrix is smaller than or equal to a preset value, determining the channel eigenvector of the current beamforming according to the autocorrelation matrix corresponding to the current beamforming, the channel eigenvector of the previous beamforming and the channel eigenvalue.

In one embodiment, the current beamforming is a beamforming for an arbitrary time unit, and the previous beamforming is a beamforming for a previous time unit of the arbitrary time unit.

In one embodiment, the current beamforming is beamforming for a specified frequency point, and the previous beamforming is beamforming for an adjacent frequency point separated from the specified frequency point by a frequency domain beamforming granularity.

In one embodiment, the beamforming the downlink signal according to the channel eigenvector of the current beamforming includes:

And carrying out normalization processing on the channel characteristic vector of the current beamforming, determining a beamforming weight according to the normalized channel characteristic vector, and carrying out beamforming on the downlink signal according to the beamforming weight.

In a second aspect, the present application provides a beamforming apparatus, the apparatus comprising a memory, a transceiver, and a processor;

The memory is used for storing a computer program;

The transceiver is used for receiving and transmitting data under the control of the processor;

the processor is configured to read a computer program stored in the memory and perform the following operations:

aiming at the ith channel characteristic vector of the current beamforming, taking the ith channel characteristic vector of the previous beamforming as an initial vector, and taking the ith channel characteristic value of the previous beamforming as an initial characteristic value, wherein the value of i traverses all integers from 1 to n, the value of i is 1 to n, n is the number of streams of the current beamforming, and n is a positive integer;

In a third aspect, the present application provides a beamforming apparatus, comprising:

the receiving unit is used for receiving the uplink reference signal and determining an autocorrelation matrix of the channel estimation corresponding to the current beam forming according to the uplink reference signal;

the determining unit is used for determining the channel eigenvector of the current beamforming according to the autocorrelation matrix corresponding to the current beamforming and the channel eigenvector of the previous beamforming;

And the beamforming unit is used for performing beamforming on the downlink signal according to the channel characteristic vector of the current beamforming.

In one embodiment, the determining unit is configured to:

In one embodiment, the shaping unit is configured to:

In a fourth aspect, the present application provides a computer readable storage medium storing a computer program for causing the computer to perform the beamforming method described in any one of the possible embodiments above.

In a fifth aspect, the present application provides a computer program product comprising a computer program which, when executed by a computer or processor, implements a beamforming method as described in any one of the possible embodiments.

The beamforming method, the device, the storage medium and the program product provided by the application utilize the autocorrelation characteristics of the time domain and the frequency domain of the actual channel, and utilize the channel characteristic vector of the previous beamforming to calculate the channel characteristic vector of the current beamforming, thereby reducing the calculated amount in the beamforming process and improving the efficiency.

It should be understood that the description of the invention above is not intended to limit key or critical features of embodiments of the invention, nor to limit the scope of the invention. Other features of the present invention will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the application or the technical solutions of the prior art, the following description of the embodiments or the drawings used in the description of the prior art will be given in brief, it being obvious that the drawings in the description below are some embodiments of the application and that other drawings can be obtained from them without inventive effort for a person skilled in the art.

Fig. 1 is a schematic diagram of time domain correlation of a CDL-C channel according to an embodiment of the present application;

fig. 2 is a schematic diagram of time domain correlation of a CDL-D channel according to an embodiment of the present application;

fig. 3 is a schematic diagram of frequency domain correlation of a CDL-C channel according to an embodiment of the present application;

fig. 4 is a schematic diagram of frequency domain correlation of a CDL-D channel according to an embodiment of the present application;

fig. 5 is a schematic diagram of a time domain feature value of a CDL-C channel according to an embodiment of the present application;

Fig. 6 is a second schematic diagram of a time domain feature value of a CDL-C channel according to an embodiment of the present application;

fig. 7 is a schematic diagram of a frequency domain feature value of a CDL-C channel according to an embodiment of the present application;

fig. 8 is a second schematic diagram of a frequency domain feature value of a CDL-C channel according to an embodiment of the present application;

Fig. 9 is a schematic flow chart of a beamforming method according to an embodiment of the present application;

Fig. 10 is a schematic structural diagram of a beamforming device according to an embodiment of the present application;

fig. 11 is a schematic structural diagram of a beamforming apparatus according to an embodiment of the present application.

Detailed Description

The term "and/or" in the present application describes an association relationship of association objects, and indicates that three relationships may exist, for example, a and/or B may indicate that a exists alone, a and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. The term "plurality" in embodiments of the present application means two or more, and other adjectives are similar.

The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

The embodiment of the application provides a beam forming method and a beam forming device so as to improve the beam forming efficiency. The method and the device are based on the same application, and because the principles of solving the problems by the method and the device are similar, the embodiments of the device and the method can be referred to each other, and the repetition is omitted.

The technical scheme provided by the embodiment of the application can be suitable for various systems, in particular to a 5G system. For example, applicable systems may be global system for mobile communications (global system of mobile communication, GSM), code division multiple access (code division multiple access, CDMA), wideband code division multiple access (Wideband Code Division Multiple Access, WCDMA) universal packet Radio service (GENERAL PACKET Radio service, GPRS), long term evolution (long term evolution, LTE), LTE frequency division duplex (frequency division duplex, FDD), LTE time division duplex (time division duplex, TDD), long term evolution-advanced (long term evolution advanced, LTE-a), universal mobile system (universal mobile telecommunication system, UMTS), worldwide interoperability for microwave access (worldwide interoperability for microwave access, wiMAX), 5G New air interface (New Radio, NR) systems, and the like. Terminal devices and network devices are included in these various systems. Core network parts may also be included in the system, such as Evolved packet system (Evolved PACKET SYSTEM, EPS), 5G system (5 GS), etc.

The terminal device according to the embodiment of the present application may be a device that provides voice and/or data connectivity to a user, a handheld device with a wireless connection function, or other processing devices connected to a wireless modem, etc. The names of the terminal devices may also be different in different systems, for example in a 5G system, the terminal devices may be referred to as User Equipment (UE). The wireless terminal device may communicate with one or more Core Networks (CNs) via a radio access Network (Radio Access Network, RAN), which may be mobile terminal devices such as mobile phones (or "cellular" phones) and computers with mobile terminal devices, e.g., portable, pocket, hand-held, computer-built-in or vehicle-mounted mobile devices that exchange voice and/or data with the radio access Network. Such as Personal communication services (Personal Communication Service, PCS) phones, cordless phones, session initiation protocol (Session Initiated Protocol, SIP) phones, wireless local loop (Wireless Local Loop, WLL) stations, personal digital assistants (Personal DIGITAL ASSISTANT, PDA) and the like. The wireless terminal device may also be referred to as a system, subscriber unit (subscriber unit), subscriber station (subscriber station), mobile station (mobile station), remote station (remote station), access point (access point), remote terminal device (remote terminal), access terminal device (ACCESS TERMINAL), user terminal device (user terminal), user agent (user agent), user equipment (user device), and embodiments of the present application are not limited.

The network device according to the embodiment of the present application may be a base station, where the base station may include a plurality of cells for providing services for the terminal. A base station may also be called an access point or may be a device in an access network that communicates over the air-interface, through one or more sectors, with wireless terminal devices, or other names, depending on the particular application. For example, the base station according to the embodiment of the present application may be an evolved network device (evolutional Node B, eNB or e-NodeB) in a long term evolution (long term evolution, LTE) system, a 5G base station (gNB) in a 5G network architecture (next generation system), or a home evolved base station (Home evolved Node B, heNB), a relay node (relay node), a home base station (femto), a pico base station (pico), or the like, which is not limited in the embodiment of the present application. In some network structures, a base station may include a centralized unit (centralized unit, CU) node and a Distributed Unit (DU) node, which may also be geographically separated.

Multiple-input Multiple-output (Multi Input Multi Output, MIMO) transmissions may each be made between the base station and the terminal device using one or more antennas, and the MIMO transmissions may be Single User MIMO (SU-MIMO) or Multiple User MIMO (MU-MIMO). The MIMO transmission may be 2D-MIMO, 3D-MIMO, FD-MIMO, or massive-MIMO, or may be diversity transmission, precoding transmission, beamforming transmission, or the like, depending on the form and number of the root antenna combinations.

Currently, in non-codebook beamforming, EVD operations often employ an exponentiation method, which is an iterative algorithm that begins to iterate, usually with a random non-zero initial vector, to obtain the eigenvectors of the matrix. Briefly introducing the exponentiation method, the algorithm of the exponentiation method is as follows:

Where N is the dimension of matrix R, u _i,0 is a non-zero initial vector, taking a full 1 vector as an example in the above example, N is the number of iterations to determine a feature vector, u _i,N is the i-th converging feature vector of matrix R, lambda _i,N is the feature value corresponding to u _i,N, and II u _i,k+1 II represents the modulus of u _i,k+1. In the exponentiation method, the matrix R is updated with each feature vector determined, i.e. with the feature vector and the corresponding feature value, to determine the next feature vector.

When the algorithm is used for non-codebook beamforming, the matrix R is an autocorrelation matrix of a channel estimation matrix H obtained after the base station performs uplink channel estimation, and the channel eigenvector of the autocorrelation matrix R needs to be determined in the non-codebook beamforming, so that the beamforming weight is determined according to the eigenvector of the matrix R to perform beamforming on the downlink signal.

In the frequency dimension, the base station also needs to perform EVD operation on a narrow-band channel on a frequency domain according to the set frequency domain shaping granularity because of a broadband system.

It can be seen that, in both the time domain and the frequency domain, each time the channel eigenvector is calculated, the iteration of the above-mentioned exponentiation method is performed by using a non-zero initial vector, however, since the initial vector is a randomly set vector, in order to ensure the iteration accuracy, the number of iterations N in the above-mentioned algorithm needs to be set to a larger value, resulting in a huge iteration operand, so that the calculation process of beamforming is inefficient.

In order to solve the above-mentioned problems, in the embodiments of the present application, the calculation amount in beamforming is reduced and the efficiency is improved by taking into consideration the autocorrelation of the time domain and the frequency domain of the actual channel, and the autocorrelation of the time domain and the frequency domain of the channel is described first. The time-varying and frequency-selective properties of the wireless channel are related to the actual wave propagation environment, and the channel characteristic values may be considered to be slowly varying when there are fewer moving objects in the wireless channel, or when the communication user and environmental objects are not moving too fast (e.g., open areas, pedestrians and vehicles at speeds <60km/h, etc.). In millisecond delay (for example, 1 SRS period), the variation of the channel characteristic value is small, in this case, the shaping weight at the current moment has higher correlation with the shaping weight at the last detection moment. The following description is made from the time domain and the frequency domain, respectively.

The most direct way to reflect the severity of the channel variation is to observe the autocorrelation of the channel, i.e., the time autocorrelation and the frequency autocorrelation. Channel time domain correlation based on the CDL model of 3gpp 38.901 is illustrated as in fig. 1 and 2. Wherein fig. 1 corresponds to a CDL-C channel, fig. 2 corresponds to a CDL-D channel, and fig. 1 and 2 are channel correlation coefficients at a certain user port time interval of 5ms, 10ms, 15ms, and 40ms in case of a user moving speed of 15 km/h. In fig. 1 and 2, the horizontal axis represents frequency, and the vertical axis represents correlation coefficient. Curves 11, 12, 13, 14 in fig. 1 correspond to time intervals of 5ms, 10ms, 15ms, 40ms, respectively, and curves 21, 22, 23, 24 in fig. 2 also correspond to time intervals of 5ms, 10ms, 15ms, 40ms, respectively. It can be seen from fig. 2 that the channel correlation coefficient of the CDL-D channel at different time intervals is mostly above 0.98, and that in the worst case, the CDL-C channel is mostly above 0.4. The above results indicate that CDL channel vectors can maintain high time-domain correlation over time intervals on the order of milliseconds.

The channel frequency domain correlation based on the CDL model of 3gpp 38.901 is illustrated as in fig. 3 and 4. Wherein, fig. 3 corresponds to a CDL-C channel, fig. 4 corresponds to a CDL-D channel, and fig. 3 and fig. 4 are correlation coefficients of channel vectors corresponding to a central frequency point and left and right frequency points of the CDL channel within a bandwidth of 100MHz at a certain moment. In fig. 3 and 4, the horizontal axis represents frequency points, and the vertical axis represents correlation coefficients. Curves 31, 32, 33, 34 in fig. 3 correspond to different ports, respectively, and curves 41, 42, 43, 44 in fig. 4 correspond to different ports, respectively. As can be seen from FIG. 4, the correlation coefficient of the CDL-D whole band channel is mostly above 0.92, and the CDL-C channel is slightly complex, but as can be seen from FIG. 3, the correlation coefficient can be maintained above 0.8 in a small frequency range (within 10MHz bandwidth). The above results indicate that the channel vectors of the CDL channel in a small frequency range (within a 10MHz bandwidth) can maintain a high frequency domain correlation.

Through the correlation analysis, the EBB forming weight based on the channel estimation can keep strong historical characteristics in the small time-frequency granularity or the small frequency-domain granularity. For the eigenvalue variation of the channel in the time domain and the frequency domain, the upper boundary of the eigenvalue variation can be approximately determined by using Wielandt-Hoffman theorem and related deductions. If matrix a is subject to perturbation of symmetric matrix E, then there are:

Where lambda _i (a) represents the i-th eigenvalue of matrix a. The inequality (1) is Wielandt-Hoffman theorem, which indicates that the sum of squares of eigenvalue changes after the matrix is perturbed does not exceed the square of the F-norm of the perturbation matrix. The above inequality (1) is further deduced as:

λ_i(A)+λ_n(E)≤λ_i(A+E)≤λ_i(A)+λ₁(E) (2)

Where n is the dimension of matrix A. The inequality (2) above gives the upper and lower bounds of the eigenvalues of matrix a+e after perturbation. From the above inequality (32), it can be further obtained that:

|λ_i(A+E)-λ_i(A)|≤max{|λ_n(E)|,|λ₁(E)|}＝‖E‖₂≤‖E‖_F (3)

Equation (3) shows that the absolute value of the eigenvalue variation of matrix a+e after perturbation does not exceed the 2-norm of the perturbation matrix.

The time domain eigenvalues of the different ports of the CDL-C channel are illustrated in fig. 5 and 6. Wherein, fig. 5 shows the channel eigenvalues in the time domain 600ms, and fig. 6 shows the channel eigenvalues in the time domain 80ms-120ms in fig. 5 within 40 ms. As can be seen from fig. 5 and 6, although the channel characteristic value fluctuates more frequently over a long period of time, the variation in the channel characteristic value is not noticeable in a smaller time domain interval range.

The frequency domain eigenvalues of the different ports of the CDL-C channel are illustrated in fig. 7 and 8. Wherein, fig. 6 is a characteristic value of a channel in a frequency domain of 500MHz bandwidth, and fig. 8 is a characteristic value in a 40MHz bandwidth in 100MHz-140MHz in fig. 7. As can be seen from fig. 7 and 8, although the channel characteristic value fluctuates more frequently in the full bandwidth, the variation of the channel characteristic value is not significant in the smaller frequency domain interval range.

As can be seen from the above correlation analysis of the time domain and the frequency domain of the channel, for the channel with weak time-varying and frequency-selective properties, the channel characteristic value can be considered to be slowly varying in a small time-frequency granularity, and the channel characteristic vectors have high correlation. Therefore, the application proposes to take the channel characteristic vector in the last beamforming as the initial vector of the EVD operation, thereby achieving the purpose of optimizing the calculation of the EBB beamforming weight by fully utilizing the channel autocorrelation characteristic and improving the calculation efficiency of the beamforming. Based on the above-mentioned conception, the beamforming method provided by the present application will be described in detail by specific embodiments. It is to be understood that the following embodiments may be combined with each other and that some embodiments may not be repeated for the same or similar concepts or processes.

Fig. 9 is a schematic flow chart of a beamforming method according to an embodiment of the present application. As shown in fig. 1, the method includes:

s901, receiving an uplink reference signal, and determining an autocorrelation matrix of channel estimation corresponding to the current beamforming according to the uplink reference signal.

The base station performs uplink channel estimation according to the uplink reference signal to obtain a channel estimation matrix, and further obtains an autocorrelation matrix of the channel estimation matrix, and the channel estimation process in this step is similar to that in the related art, which is not described herein specifically.

S902, determining the channel eigenvector of the current beamforming according to the autocorrelation matrix corresponding to the current beamforming and the channel eigenvector of the previous beamforming.

It has been mentioned in the foregoing description that the beamforming may be performed in the time domain and the frequency domain, where when the beamforming is performed in the time domain, the current beamforming is the beamforming for an arbitrary time unit, and the previous beamforming is the beamforming for a previous time unit of the arbitrary time unit, where the time unit may be any one of a Transmission time interval (Transmission TIME INTERVAL, TTI), a frame, a time slot, or a symbol. When the frequency domain performs beamforming, the current beamforming is the beamforming aiming at the appointed frequency point, and the previous beamforming is the beamforming aiming at the adjacent frequency point with a frequency domain beamforming granularity which is separated from the appointed frequency point. In the following description of the embodiment of the present application, the time domain or the frequency domain is not particularly limited, and only the current time and the previous time are used for description, and it is understood that the description may represent the current time and the previous time in the time domain, or may represent the current time and the previous time in the frequency domain.

As can be seen from the above correlation analysis of the time domain and the frequency domain of the channel, the channel eigenvalues are slowly changed in a smaller time-frequency granularity, and the channel eigenvectors have higher correlation. Therefore, in the embodiment of the application, when determining the channel eigenvector of the current beamforming, the channel eigenvector of the previous beamforming is combined, for example, in the iterative process of determining the channel eigenvector of the current beamforming, the channel eigenvector of the previous beamforming is used as the initial vector of iteration, and because the channel eigenvectors of the two beamforming have higher correlation, the iterative times can be greatly reduced.

S903, carrying out beam forming on the downlink signal according to the channel characteristic vector of the current beam forming.

After determining the channel feature vector of the current beamforming, the normalization processing may be performed on the channel feature vector of the current beamforming, the beamforming weight may be determined according to the normalized channel feature vector, and the downlink signal may be beamformed according to the beamforming weight.

According to the beam forming method provided by the embodiment of the application, the channel characteristic vector of the current beam forming is calculated by utilizing the autocorrelation characteristics of the time domain and the frequency domain of the actual channel and utilizing the channel characteristic vector of the previous beam forming, so that the calculated amount in the beam forming process can be reduced, and the efficiency is improved.

On the basis of the above embodiment, description is made on how to determine the channel eigenvector of the current beamforming according to the autocorrelation matrix corresponding to the current beamforming and the channel eigenvector of the previous beamforming by combining the exponentiation method.

It should be noted that, the number of channel eigenvectors in the current beamforming is n, n is the number of streams in the current beamforming, that is, the dimension of the autocorrelation matrix, and n is a positive integer. When the exponentiation method is adopted to determine the channel eigenvector of the current beamforming, the channel eigenvector of the previous beamforming is used as the initial vector in the exponentiation method, namely:

The method comprises the steps of taking the ith channel eigenvector of the current beamforming as an initial vector, traversing all integers from 1 to n in the value of i, taking the vector obtained by multiplying the initial vector by an autocorrelation matrix corresponding to the current beamforming as a new initial vector, repeatedly executing the steps until a first preset number of times is reached, determining the obtained vector as the ith channel eigenvector of the current beamforming, determining the ith channel eigenvalue of the current beamforming according to the ith channel eigenvector of the current beamforming and the autocorrelation matrix corresponding to the current beamforming, updating the autocorrelation matrix corresponding to the current beamforming according to the ith channel eigenvalue of the current beamforming and the ith channel eigenvector of the current beamforming, and determining the (i+1) th channel eigenvector of the current beamforming by executing the steps.

An example algorithm is as follows:

The superscript (t) represents an arbitrary time unit, the superscript (t-1) represents a time unit before the arbitrary time unit, and the algorithm only takes a time domain as an example, and when the algorithm calculates in a frequency domain, the superscript representing the arbitrary time unit is only required to be modified into the superscript representing a designated frequency point, and the superscript representing the previous time unit of the arbitrary time unit is only required to be modified into the superscript representing a previous adjacent frequency point of the designated frequency point.

Wherein R ^(t) is the autocorrelation matrix of time unit t,The i-th converged channel characteristic vector representing time unit t-1,An ith converged channel characteristic vector representing time unit t,The i-th channel characteristic value of the time unit t is represented. In the algorithm, the channel characteristic vector at the previous moment is used as an initial vector of iteration when the channel characteristic vector is calculated each time, so that the setting of the iteration times N can be reduced. Since most of the computation is performed in an iterative loop, the number of iterations can be used as an indicator of the complexity of the comparison algorithm before and after improvement. Simulation shows that the total iteration number after improvement under the CDL-D channel is about 17.77% of the original iteration number after improvement under the CDL-C channel is about 36.29% of the original iteration number on the premise of not losing the shaping performance, so that the algorithm iteration number is greatly reduced, the algorithm convergence speed is accelerated, and the efficiency is improved.

In addition to the above improvement of the exponentiation method, another calculation method of the channel eigenvector may be provided in the embodiment of the present application, where, in addition to using the channel eigenvector of the last beamforming as the initial vector of the iteration, the eigenvalue of the last beamforming may also be used as the initial eigenvalue of the iteration. That is, the channel eigenvector of the current beamforming is determined according to the autocorrelation matrix corresponding to the current beamforming, and the channel eigenvector and the channel eigenvalue of the previous beamforming.

In this case, the iterative computation may be performed by an inverse iterative method, which is particularly suitable for solving the problem of the eigenvector by using the known eigenvalue approximation, as mentioned above, the variation of the eigenvalue of the channel formed by the two beamforming is often not very large. The advantage of using the inverse iteration method is that the convergence speed is fast, and the number of iterations required for convergence is less than that of the exponentiation method. The process for this method is as follows:

Aiming at the ith channel eigenvector of the current beamforming, taking the ith channel eigenvector of the previous beamforming as an initial vector, taking the ith channel eigenvalue of the previous beamforming as an initial eigenvalue, traversing all integers from 1 to n, wherein n is the number of streams of the current beamforming, n is a positive integer, determining an inverse matrix according to an autocorrelation matrix corresponding to the current beamforming and the initial eigenvalue, taking a vector obtained by multiplying the initial vector by the inverse matrix as a new initial vector, repeatedly executing the steps until the second preset times is reached, determining the obtained vector as the ith channel eigenvector of the current beamforming, determining the ith channel eigenvalue of the current beamforming according to the ith channel eigenvector of the current beamforming and the autocorrelation matrix corresponding to the current beamforming, and executing the steps to determine the n channel eigenvectors of the current beamforming.

An example algorithm is as follows:

In this algorithm, similarly to the foregoing, the superscript (t) indicates an arbitrary time unit, the superscript (t-1) indicates a time unit preceding the arbitrary time unit, and the foregoing algorithm only takes the time domain as an example, and when calculating in the frequency domain, it is only necessary to modify the superscript indicating the arbitrary time unit into the superscript indicating the designated frequency point, and modify the superscript indicating the previous time unit of the arbitrary time unit into the superscript indicating the previous adjacent frequency point of the designated frequency point.

Wherein R ^(t) is the autocorrelation matrix of time unit t, I is the identity matrix,As an inverse matrix of the time unit t,The i-th converged channel characteristic vector representing time unit t-1,The i-th channel characteristic value representing time unit t-1,An ith converged channel characteristic vector representing time unit t,The i-th channel characteristic value of the time unit t is represented. In the algorithm, the channel characteristic vector of the previous time unit is used as an initial vector of iteration and the channel characteristic value of the previous time unit is used as an initial characteristic value of iteration when the channel characteristic vector is calculated each time, so that the setting of the iteration times N can be further reduced. For example, the number of simulated verification iterations N may be set to 4. Pairs of the above algorithmThe normalization operation of (a) can be in or out of the iterative process, and as the example is out of the iterative process, the number of modulo operation times can be reduced, the calculated amount is reduced, and the efficiency is improved.

The calculation matrix in the algorithmWhen the inversion object is a symmetric matrix determined by using the erl Mi Tezheng, and assuming that the dimension of the matrix is 4×4, a fast matrix inversion algorithm based on Cholesky decomposition can be used. Implementation of Cholesky decomposition may be accomplished using a chunking method, with 4 operations. Each operation includes calculating a certain 1 column of the lower triangular matrix resulting from Cholesky decomposition and updating a part of the elements of the original matrix. Inverting the resulting lower triangular matrix may also be accomplished by 4 operations, each of which computes 1 diagonal of the lower triangular inverse matrix.

The above embodiments describe that the improved exponentiation method or the inverse iteration method is adopted to calculate the channel eigenvectors, and in practical application, the base station can select one of them to calculate according to practical situations. When the channel suddenly changes, such as multipath extinction in a time-varying channel, LOS and NLOS scene switching (or frequency domain frequency selectivity is high), the channel characteristic values of different time units (or frequency points) are easy to suddenly change, and the effect of the initial characteristic values and the characteristic vectors is invalid, so that the inverse iteration method may converge to the same characteristic vector or the convergence speed is slow, and the calculation can be performed by adopting the exponentiation method. The situation can be pre-judged by using the inference of Wielandt-Hoffman theorem introduced above, namely, a difference matrix of the channel estimation autocorrelation matrix corresponding to the current beamforming and the channel estimation autocorrelation matrix corresponding to the previous beamforming is determined, and if the 2-norm of the difference matrix is smaller than or equal to a preset value, the channel eigenvector of the current beamforming is determined according to the autocorrelation matrix corresponding to the current beamforming, the channel eigenvector of the previous beamforming and the channel eigenvalue.

The inequality (3) introduced above illustrates that if the square matrix a is subject to perturbation of the symmetric square matrix E, the absolute value of the eigenvalue variation of the matrix a+e after the perturbation does not exceed the 2-norm of the perturbation matrix. In the beamforming scene applied to the embodiment of the application, the square matrix A is the autocorrelation matrix of the channel estimation corresponding to the previous beamforming, the matrix A+E is the autocorrelation matrix of the channel estimation corresponding to the current beamforming, and the upper limit of the characteristic value change of the matrix A+E and the square matrix A is the 2-norm of the difference matrix E of the matrix A and the square matrix A. Under the condition that the 2-norm of the difference matrix is smaller than or equal to a preset value, the characteristic value of the current beamforming and the characteristic value of the previous beamforming are not changed greatly, an inverse iteration method can be adopted at the moment, under the condition that the 2-norm of the difference matrix is larger than the preset value, the characteristic value of the current beamforming and the characteristic value of the previous beamforming are determined to be changed greatly, and a power method can be adopted at the moment. The magnitude of the preset value can be set according to the actual condition of the channel.

Fig. 10 is a schematic structural diagram of a beamforming apparatus according to an embodiment of the present application. As shown in fig. 10, the beamforming device comprises a memory 101, a transceiver 102, and a processor 103.

A memory 101 for storing a computer program.

A transceiver 102 for receiving and transmitting data under the control of the processor.

A processor 103 for reading the computer program stored in the memory and performing the following operations:

Receiving an uplink reference signal, and determining an autocorrelation matrix of channel estimation corresponding to the current beam forming according to the uplink reference signal;

In one embodiment, determining the channel eigenvector of the current beamforming according to the autocorrelation matrix corresponding to the current beamforming and the channel eigenvector of the previous beamforming includes:

Taking a vector obtained by multiplying the initial vector by an autocorrelation matrix corresponding to the current beamforming as a new initial vector, repeatedly executing the step until the first preset times are reached, and determining the obtained vector as an ith channel characteristic vector of the current beamforming;

Determining an ith channel characteristic value of the current beamforming according to an ith channel characteristic vector of the current beamforming and an autocorrelation matrix corresponding to the current beamforming, updating the autocorrelation matrix corresponding to the current beamforming according to the ith channel characteristic value of the current beamforming and the ith channel characteristic vector of the current beamforming, and determining an (i+1) th channel characteristic vector of the current beamforming by the updated autocorrelation matrix;

and determining the channel eigenvector of the current beamforming according to the autocorrelation matrix corresponding to the current beamforming and the channel eigenvector and the channel eigenvalue of the previous beamforming.

In one embodiment, determining the channel eigenvector of the current beamforming according to the autocorrelation matrix corresponding to the current beamforming, the channel eigenvector of the previous beamforming, and the channel eigenvalue includes:

Determining a difference matrix of an autocorrelation matrix of channel estimation corresponding to the current beamforming and an autocorrelation matrix of channel estimation corresponding to the previous beamforming;

In one embodiment, the current beamforming is a beamforming for a designated frequency point, and the previous beamforming is a beamforming for an adjacent frequency point separated from the designated frequency point by a frequency domain beamforming granularity.

In one embodiment, beamforming is performed on a downlink signal according to a channel eigenvector of the current beamforming, including:

And carrying out normalization processing on the channel eigenvectors of the current beamforming, determining a beamforming weight according to the normalized channel eigenvectors, and carrying out beamforming on the downlink signals according to the beamforming weight.

It should be noted that, the above device provided by the present application can implement all the method steps in the method embodiment, and can achieve the same technical effects, and specific details of the same parts and beneficial effects as those of the method embodiment in the present embodiment are not repeated here.

Fig. 11 is a schematic structural diagram of a beamforming apparatus according to an embodiment of the present application. As shown in fig. 11, the beamforming apparatus includes:

a receiving unit 111, configured to receive an uplink reference signal, and determine an autocorrelation matrix of a channel estimation corresponding to the current beamforming according to the uplink reference signal;

A determining unit 112, configured to determine a channel feature vector of the current beamforming according to the autocorrelation matrix corresponding to the current beamforming and a channel feature vector of the previous beamforming;

and the beamforming unit 113 is configured to perform beamforming on the downlink signal according to the channel eigenvector of the current beamforming.

In one embodiment, the determining unit 112 is configured to:

In one embodiment, the shaping unit 113 is configured to:

It should be noted that, in the embodiment of the present application, the division of the units is schematic, which is merely a logic function division, and other division manners may be implemented in actual practice. In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units described above, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a processor-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. The storage medium includes a U disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, an optical disk, or other various media capable of storing program codes.

The embodiment of the application also provides a processor-readable storage medium, wherein the processor-readable storage medium stores a computer program, and the computer program is used for enabling a processor to execute the beam forming method in any one of the possible implementation manners.

The embodiments of the present application also provide a computer program product comprising a computer program which, when executed by a processor, implements the beamforming method in any one of the possible embodiments.

It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-executable instructions. These computer-executable instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These processor-executable instructions may also be stored in a processor-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the processor-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These processor-executable instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims

1. A method of beamforming, the method comprising:

Determining a channel characteristic vector of current beam forming according to the autocorrelation matrix corresponding to the current beam forming and a channel characteristic vector of previous beam forming, wherein the current beam forming is beam forming aiming at any time unit, the previous beam forming is beam forming aiming at the previous time unit of any time unit, or the current beam forming is beam forming aiming at a specified frequency point, and the previous beam forming is beam forming aiming at an adjacent frequency point separated from the specified frequency point by a frequency domain forming granularity;

2. The method according to claim 1, wherein the determining the channel eigenvector of the current beamforming according to the autocorrelation matrix corresponding to the current beamforming and the channel eigenvector of the previous beamforming comprises:

and repeatedly executing the steps to determine the channel characteristic vector of the current beam forming.

3. The method according to claim 1, wherein the determining the channel eigenvector of the current beamforming according to the autocorrelation matrix corresponding to the current beamforming and the channel eigenvector of the previous beamforming comprises:

4. The method according to claim 1, wherein the determining the channel eigenvector of the current beamforming according to the autocorrelation matrix corresponding to the current beamforming and the channel eigenvector of the previous beamforming comprises:

5. A beam forming device, which is characterized by comprising a memory, a transceiver and a processor;

The memory is used for storing a computer program;

6. The apparatus of claim 5, wherein the determining the channel eigenvector of the current beamforming according to the autocorrelation matrix corresponding to the current beamforming and the channel eigenvector of the previous beamforming comprises:

7. The apparatus of claim 5, wherein the determining the channel eigenvector of the current beamforming according to the autocorrelation matrix corresponding to the current beamforming and the channel eigenvector of the previous beamforming comprises:

8. The apparatus of claim 5, wherein the determining the channel eigenvector of the current beamforming according to the autocorrelation matrix corresponding to the current beamforming and the channel eigenvector of the previous beamforming comprises:

9. A beamforming apparatus, the apparatus comprising:

The device comprises a determining unit, a processing unit and a processing unit, wherein the determining unit is used for determining the channel eigenvector of the current beam forming according to the autocorrelation matrix corresponding to the current beam forming and the channel eigenvector of the previous beam forming, wherein the current beam forming is the beam forming aiming at any time unit, the previous beam forming is the beam forming aiming at the previous time unit of any time unit, or the current beam forming is the beam forming aiming at a specified frequency point, and the previous beam forming is the beam forming aiming at an adjacent frequency point with a frequency domain forming granularity spaced from the specified frequency point;

and the shaping unit is used for carrying out normalization processing on the channel eigenvectors of the current beam shaping, determining shaping weights according to the normalized channel eigenvectors, and carrying out beam shaping on downlink signals according to the shaping weights.

10. The apparatus according to claim 9, wherein the determining unit is configured to:

11. The apparatus according to claim 9, wherein the determining unit is configured to:

12. The apparatus according to claim 9, wherein the determining unit is configured to:

13. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program for causing a computer to perform the method according to any one of claims 1-4.

14. A computer program product, characterized in that the computer program product comprises a computer program which, when executed by a computer or processor, implements the method according to any of claims 1-4.