A beamforming method, device, storage medium and program product

Abstract

The present application provides a beamforming method, device, storage medium and program product. The method includes: receiving an uplink reference signal, and determining the autocorrelation matrix of the channel estimation corresponding to the current beamforming according to the uplink reference signal; determining the channel characteristic vector of the current beamforming according to the autocorrelation matrix corresponding to the current beamforming and the channel characteristic vector of the previous beamforming; and beamforming the downlink signal according to the channel characteristic vector of the current beamforming. Thus, the beamforming efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a beamforming method, device, storage medium and program product. Background Art

[0002] Beamforming is to adjust the weight vector of each antenna in the antenna array to concentrate the signal power in a certain direction, reduce spatial interference and improve link quality.

[0003] In non-codebook beamforming, eigenvalue based beamforming (EBB) is usually used. This beamforming method requires continuous eigenvalue decomposition (EVD) of the channel. Specifically, the base station performs EVD operation after obtaining the uplink sounding reference signal (SRS) channel estimation, and determines the beamforming weight according to the channel eigenvector obtained by the EVD operation, so as to perform beamforming processing on the signal according to the beamforming weight. In practical applications, when the channel eigenvector is obtained by EVD operation, the power method is usually used to start iteration with a random non-zero initial vector to obtain the channel eigenvector, which is inefficient. Summary of the invention

[0004] The present application provides a beamforming method, device, storage medium and program product to improve the efficiency of beamforming.

[0005] In a first aspect, the present application provides a beamforming method, comprising:

[0006] receiving an uplink reference signal, and determining an autocorrelation matrix of a channel estimation corresponding to the current beamforming according to the uplink reference signal;

[0007] Determining a channel eigenvector for this beamforming according to the autocorrelation matrix corresponding to this beamforming and a channel eigenvector for a previous beamforming;

[0008] The downlink signal is beamformed according to the channel eigenvector of this beamforming.

[0009] In one implementation, 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:

[0010] For the i-th channel eigenvector of the current beamforming, the i-th channel eigenvector of the previous beamforming is used as the initial vector, wherein the value of i traverses all integers between 1 and n, n is the number of streams of the current beamforming, and n is a positive integer;

[0011] A vector obtained by multiplying the initial vector by the autocorrelation matrix corresponding to the current beamforming is used as a new initial vector, and this step is repeated until a first preset number of times is reached, and the obtained vector is determined as the i-th channel eigenvector of the current beamforming;

[0012] Determine the i-th channel eigenvalue of the current beamforming according to the i-th channel eigenvector of the current beamforming and the autocorrelation matrix corresponding to the current beamforming, and update the autocorrelation matrix corresponding to the current beamforming according to the i-th channel eigenvalue of the current beamforming and the i-th channel eigenvector of the current beamforming, and the updated autocorrelation matrix is ​​used to determine the i+1-th channel eigenvector of the current beamforming;

[0013] Execute the above steps to determine the channel eigenvector of this beamforming.

[0014] In one implementation, 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:

[0015] The channel eigenvector of the current beamforming is determined according to the autocorrelation matrix corresponding to the current beamforming, and the channel eigenvector and channel eigenvalue of the previous beamforming.

[0016] In one implementation, determining the channel eigenvector of the current beamforming according to the autocorrelation matrix corresponding to the current beamforming and the channel eigenvector and channel eigenvalue of the previous beamforming includes:

[0017] For the i-th channel eigenvector of this beamforming, the i-th channel eigenvector of the previous beamforming is used as the initial vector, and the i-th channel eigenvalue of the previous beamforming is used as the initial eigenvalue, wherein the value of i traverses all integers between 1 and n, and n is the number of streams of this beamforming, and n is a positive integer;

[0018] Determine an inverse matrix according to the autocorrelation matrix corresponding to the current beamforming and the initial eigenvalue;

[0019] A vector obtained by multiplying the initial vector by the inverse matrix is ​​used as a new initial vector, and this step is repeated until a second preset number of times is reached, and the obtained vector is determined as the i-th channel eigenvector of this beamforming, and the i-th channel eigenvalue of this beamforming is determined according to the i-th channel eigenvector of this beamforming and the autocorrelation matrix corresponding to this beamforming;

[0020] Execute the above steps to determine the channel eigenvector of this beamforming.

[0021] In one implementation, determining the channel eigenvector of the current beamforming according to the autocorrelation matrix corresponding to the current beamforming and the channel eigenvector and channel eigenvalue of the previous beamforming includes:

[0022] Determine a difference matrix between 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;

[0023] When the 2-norm of the difference matrix is ​​less than or equal to a preset value, the channel eigenvector of this beamforming is determined according to the autocorrelation matrix corresponding to this beamforming, and the channel eigenvector and channel eigenvalue of the previous beamforming.

[0024] In one implementation, the current beamforming is beamforming for an arbitrary time unit, and the previous beamforming is beamforming for a time unit before the arbitrary time unit.

[0025] In one implementation, the current beamforming is beamforming for a designated frequency point, and the previous beamforming is beamforming for an adjacent frequency point that is separated from the designated frequency point by a frequency domain beamforming granularity.

[0026] In one implementation, the beamforming of the downlink signal according to the channel eigenvector of the current beamforming includes:

[0027] The channel characteristic vector of this beamforming is normalized, a beamforming weight is determined according to the normalized channel characteristic vector, and beamforming is performed on the downlink signal according to the beamforming weight.

[0028] In a second aspect, the present application provides a beamforming device, the device comprising: a memory, a transceiver, and a processor;

[0029] The memory is used to store computer programs;

[0030] The transceiver is used to send and receive data under the control of the processor;

[0031] The processor is configured to read the computer program stored in the memory and perform the following operations:

[0032] receiving an uplink reference signal, and determining an autocorrelation matrix of a channel estimation corresponding to the current beamforming according to the uplink reference signal;

[0033] Determining a channel eigenvector for this beamforming according to the autocorrelation matrix corresponding to this beamforming and a channel eigenvector for a previous beamforming;

[0034] The downlink signal is beamformed according to the channel eigenvector of this beamforming.

[0035] In one implementation, 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:

[0036] For the i-th channel eigenvector of the current beamforming, the i-th channel eigenvector of the previous beamforming is used as the initial vector, wherein the value of i traverses all integers between 1 and n, n is the number of streams of the current beamforming, and n is a positive integer;

[0037] A vector obtained by multiplying the initial vector by the autocorrelation matrix corresponding to the current beamforming is used as a new initial vector, and this step is repeated until a first preset number of times is reached, and the obtained vector is determined as the i-th channel eigenvector of the current beamforming;

[0038] Determine the i-th channel eigenvalue of the current beamforming according to the i-th channel eigenvector of the current beamforming and the autocorrelation matrix corresponding to the current beamforming, and update the autocorrelation matrix corresponding to the current beamforming according to the i-th channel eigenvalue of the current beamforming and the i-th channel eigenvector of the current beamforming, and the updated autocorrelation matrix is ​​used to determine the i+1-th channel eigenvector of the current beamforming;

[0039] Execute the above steps to determine the channel eigenvector of this beamforming.

[0040] In one implementation, 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:

[0041] The channel eigenvector of the current beamforming is determined according to the autocorrelation matrix corresponding to the current beamforming, and the channel eigenvector and channel eigenvalue of the previous beamforming.

[0042] In one implementation, determining the channel eigenvector of the current beamforming according to the autocorrelation matrix corresponding to the current beamforming and the channel eigenvector and channel eigenvalue of the previous beamforming includes:

[0043] For the i-th channel eigenvector of this beamforming, the i-th channel eigenvector of the previous beamforming is used as the initial vector, and the i-th channel eigenvalue of the previous beamforming is used as the initial eigenvalue, wherein the value of i traverses all integers between 1 and n, the value of i is 1 to n, n is the number of streams of this beamforming, and n is a positive integer;

[0044] Determine an inverse matrix according to the autocorrelation matrix corresponding to the current beamforming and the initial eigenvalue;

[0045] A vector obtained by multiplying the initial vector by the inverse matrix is ​​used as a new initial vector, and this step is repeated until a second preset number of times is reached, and the obtained vector is determined as the i-th channel eigenvector of this beamforming, and the i-th channel eigenvalue of this beamforming is determined according to the i-th channel eigenvector of this beamforming and the autocorrelation matrix corresponding to this beamforming;

[0046] Execute the above steps to determine the channel eigenvector of this beamforming.

[0047] In one implementation, determining the channel eigenvector of the current beamforming according to the autocorrelation matrix corresponding to the current beamforming and the channel eigenvector and channel eigenvalue of the previous beamforming includes:

[0048] Determine a difference matrix between 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;

[0049] When the 2-norm of the difference matrix is ​​less than or equal to a preset value, the channel eigenvector of this beamforming is determined according to the autocorrelation matrix corresponding to this beamforming, and the channel eigenvector and channel eigenvalue of the previous beamforming.

[0050] In one implementation, the current beamforming is beamforming for an arbitrary time unit, and the previous beamforming is beamforming for a time unit before the arbitrary time unit.

[0051] In one implementation, the current beamforming is beamforming for a designated frequency point, and the previous beamforming is beamforming for an adjacent frequency point that is separated from the designated frequency point by a frequency domain beamforming granularity.

[0052] In one implementation, the beamforming of the downlink signal according to the channel eigenvector of the current beamforming includes:

[0053] The channel characteristic vector of this beamforming is normalized, a beamforming weight is determined according to the normalized channel characteristic vector, and beamforming is performed on the downlink signal according to the beamforming weight.

[0054] In a third aspect, the present application provides a beamforming device, including:

[0055] A receiving unit, 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;

[0056] A determination unit, configured to determine a channel eigenvector for this beamforming according to the autocorrelation matrix corresponding to this beamforming and a channel eigenvector for a previous beamforming;

[0057] The beamforming unit is used to perform beamforming on the downlink signal according to the channel characteristic vector of this beamforming.

[0058] In one implementation, the determining unit is configured to:

[0059] For the i-th channel eigenvector of the current beamforming, the i-th channel eigenvector of the previous beamforming is used as the initial vector, wherein the value of i traverses all integers between 1 and n, n is the number of streams of the current beamforming, and n is a positive integer;

[0060] A vector obtained by multiplying the initial vector by the autocorrelation matrix corresponding to the current beamforming is used as a new initial vector, and this step is repeated until a first preset number of times is reached, and the obtained vector is determined as the i-th channel eigenvector of the current beamforming;

[0061] Determine the i-th channel eigenvalue of the current beamforming according to the i-th channel eigenvector of the current beamforming and the autocorrelation matrix corresponding to the current beamforming, and update the autocorrelation matrix corresponding to the current beamforming according to the i-th channel eigenvalue of the current beamforming and the i-th channel eigenvector of the current beamforming, and the updated autocorrelation matrix is ​​used to determine the i+1-th channel eigenvector of the current beamforming;

[0062] Execute the above steps to determine the channel eigenvector of this beamforming.

[0063] In one implementation, the determining unit is configured to:

[0064] The channel eigenvector of the current beamforming is determined according to the autocorrelation matrix corresponding to the current beamforming, and the channel eigenvector and channel eigenvalue of the previous beamforming.

[0065] In one implementation, the determining unit is configured to:

[0066] For the i-th channel eigenvector of this beamforming, the i-th channel eigenvector of the previous beamforming is used as the initial vector, and the i-th channel eigenvalue of the previous beamforming is used as the initial eigenvalue, wherein the value of i traverses all integers between 1 and n, and n is the number of streams of this beamforming, and n is a positive integer;

[0067] Determine an inverse matrix according to the autocorrelation matrix corresponding to the current beamforming and the initial eigenvalue;

[0068] A vector obtained by multiplying the initial vector by the inverse matrix is ​​used as a new initial vector, and this step is repeated until a second preset number of times is reached, and the obtained vector is determined as the i-th channel eigenvector of this beamforming, and the i-th channel eigenvalue of this beamforming is determined according to the i-th channel eigenvector of this beamforming and the autocorrelation matrix corresponding to this beamforming;

[0069] Execute the above steps to determine the channel eigenvector of this beamforming.

[0070] In one implementation, the determining unit is configured to:

[0071] Determine a difference matrix between 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;

[0072] When the 2-norm of the difference matrix is ​​less than or equal to a preset value, the channel eigenvector of this beamforming is determined according to the autocorrelation matrix corresponding to this beamforming, and the channel eigenvector and channel eigenvalue of the previous beamforming.

[0073] In one implementation, the current beamforming is beamforming for an arbitrary time unit, and the previous beamforming is beamforming for a time unit before the arbitrary time unit.

[0074] In one implementation, the current beamforming is beamforming for a designated frequency point, and the previous beamforming is beamforming for an adjacent frequency point that is separated from the designated frequency point by a frequency domain beamforming granularity.

[0075] In one embodiment, the excipient unit is used for:

[0076] The channel characteristic vector of this beamforming is normalized, a beamforming weight is determined according to the normalized channel characteristic vector, and beamforming is performed on the downlink signal according to the beamforming weight.

[0077] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is used to enable the computer to execute the beamforming method described in any one of the possible implementation modes described above.

[0078] In a fifth aspect, the present application provides a computer program product, which includes a computer program, and when the computer program is executed by a computer or a processor, it implements the beamforming method described in any possible implementation manner.

[0079] The beamforming method, device, storage medium and program product provided in the present application utilize the autocorrelation characteristics of the actual channel in the time domain and frequency domain, and utilize the channel characteristic vector of the previous beamforming to calculate the channel characteristic vector of the current beamforming, which can reduce the amount of calculation in the beamforming process and improve efficiency.

[0080] It should be understood that the contents described in the above summary of the invention are not intended to limit the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] In order to more clearly illustrate the technical solutions in the present application or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0082] Figure 1 A schematic diagram of the time domain correlation of the CDL-C channel provided in an embodiment of the present application;

[0083] Figure 2 A schematic diagram of the time domain correlation of the CDL-D channel provided in an embodiment of the present application;

[0084] Figure 3 A schematic diagram of frequency domain correlation of a CDL-C channel provided in an embodiment of the present application;

[0085] Figure 4 A schematic diagram of frequency domain correlation of a CDL-D channel provided in an embodiment of the present application;

[0086] Figure 5Schematic diagram of the time domain characteristic value of the CDL-C channel provided in the embodiment of the present application Figure 1 ;

[0087] Figure 6 Schematic diagram of the time domain characteristic value of the CDL-C channel provided in the embodiment of the present application Figure 2 ;

[0088] Figure 7 Schematic diagram of the frequency domain characteristic value of the CDL-C channel provided in the embodiment of the present application Figure 1 ;

[0089] Figure 8 Schematic diagram of the frequency domain characteristic value of the CDL-C channel provided in the embodiment of the present application Figure 2 ;

[0090] Fig. 9 A schematic diagram of a flow chart of a beamforming method provided in an embodiment of the present application;

[0091] Fig.10 A schematic diagram of a beamforming device provided in an embodiment of the present application Figure 1 ;

[0092] Fig.11 A schematic diagram of a beamforming device provided in an embodiment of the present application Figure 2 . DETAILED DESCRIPTION

[0093] The term "and / or" in this application describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after the association are in an "or" relationship. In the embodiments of this application, the term "plurality" refers to two or more, and other quantifiers are similar.

[0094] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0095] The embodiments of the present application provide a beamforming method and device to improve the efficiency of beamforming. The method and device are based on the same application concept. Since the principles of solving problems by the method and device are similar, the embodiments of the device and method can refer to each other, and the repeated parts will not be repeated.

[0096] The technical solution provided in the embodiment of the present application can be applicable to a variety of systems, especially 5G systems. For example, the applicable system can be a global system of mobile communication (GSM) system, a code division multiple access (CDMA) system, a wideband code division multiple access (WCDMA) general packet radio service (GPRS) system, a long term evolution (LTE) system, a LTE frequency division duplex (FDD) system, a LTE time division duplex (TDD) system, an advanced long term evolution (LTE-A) system, a universal mobile telecommunication system (UMTS), a world-wide interoperability for microwave access (WiMAX) system, a 5G new air interface (NR) system, etc. These various systems include terminal equipment and network equipment. The system may also include a core network part, such as an evolved packet system (EPS), a 5G system (5GS), etc.

[0097] The terminal device involved in 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. In different systems, the name of the terminal device may also be different. For example, in a 5G system, the terminal device may be called a user equipment (UE). A wireless terminal device can communicate with one or more core networks (CN) via a radio access network (RAN). The wireless terminal device may be a mobile terminal device, such as a mobile phone (or a "cellular" phone) and a computer with a mobile terminal device. For example, it may be a portable, pocket-sized, handheld, computer-built-in or vehicle-mounted mobile device that exchanges language and / or data with a wireless access network. For example, personal communication service (PCS) phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs) and other devices. The wireless terminal device may also be referred to as a system, a subscriber unit, a subscriber station, a mobile station, a mobile station, a remote station, an access point, a remote terminal device, an access terminal device, a user terminal device, a user agent, and a user device, but is not limited in the embodiments of the present application.

[0098] The network device involved in the embodiment of the present application may be a base station, which may include multiple cells providing services for the terminal. Depending on the specific application scenario, the base station may also be called an access point, or may be a device in the access network that communicates with the wireless terminal device through one or more sectors on the air interface, or other names. For example, the base station involved in the embodiment of the present application may be an evolutionary network device (evolutional Node B, eNB or e-NodeB) in the long term evolution (LTE) system, a 5G base station (gNB) in the 5G network architecture (next generation system), or a home evolved Node B (HeNB), a relay node, a home base station (femto), a pico base station (pico), etc., which is not limited in the embodiment of the present application. In some network structures, the base station may include a centralized unit (CU) node and a distributed unit (DU) node, and the centralized unit and the distributed unit may also be arranged geographically separately.

[0099] The base station and the terminal device can each use one or more antennas for multiple-input multiple-output (MIMO) transmission. MIMO transmission can be single-user MIMO (SU-MIMO) or multi-user MIMO (MU-MIMO). Depending on the form and number of antenna combinations, MIMO transmission can be 2D-MIMO, 3D-MIMO, FD-MIMO or massive-MIMO, or it can be diversity transmission, precoding transmission or beamforming transmission, etc.

[0100] At present, in non-codebook beamforming, EVD operation often uses the power method, which is an iterative algorithm that usually starts iteration with a random non-zero initial vector to obtain the eigenvector of the matrix. The power method is briefly introduced and the algorithm of the power method is as follows:

[0101]

[0102] Where n is the dimension of matrix R; u i，0 is a non-zero initial vector. In the above example, a vector of all 1s is used as an example. N is the number of iterations to determine a feature vector. u i，N is the i-th converged eigenvector of matrix R; i，N For u i，N The corresponding eigenvalue; ‖ui，k+1 ‖ represents u i，k+1 In the power method, each time an eigenvector is determined, the matrix R is updated using the eigenvector and the corresponding eigenvalue to determine the next eigenvector.

[0103] When the above algorithm is used for non-codebook beamforming, the matrix R is the autocorrelation matrix of the channel estimation matrix H obtained after the base station performs uplink channel estimation. In the non-codebook beamforming, it is necessary to determine the channel eigenvector of the autocorrelation matrix R, and then determine the beamforming weight according to the eigenvector of the matrix R to perform beamforming on the downlink signal.

[0104] Among them, in the time dimension, the base station must perform EVD operation after obtaining the uplink channel estimate at different detection times; in the frequency dimension, since it is a broadband system, the base station also needs to perform EVD operation on the narrowband channel in the frequency domain according to the set frequency domain shaping granularity.

[0105] It can be seen that, no matter in the time domain or the frequency domain, each time the channel eigenvector is calculated, the above power method is iterated with 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 algorithm needs to be set to a large value, resulting in a huge amount of iterative calculations, making the beamforming calculation process less efficient.

[0106] In order to solve the above problems, the embodiments of the present application consider using the autocorrelation of the time domain and frequency domain of the actual channel to reduce the amount of calculation during beamforming and improve efficiency. First, the autocorrelation of the time domain and frequency domain of the channel is explained. The time variability and frequency selectivity of the wireless channel are related to the actual radio wave propagation environment. When there are fewer moving objects in the wireless channel, or the communication users and environmental objects are not moving at a high speed (such as open areas, pedestrians and vehicles with a speed of <60km / h, etc.), the channel characteristic values ​​can be considered to be slowly varying. Within a millisecond delay (such as 1 SRS cycle), the channel characteristic value changes very little. In this case, the shaping weight at the current moment has a high correlation with the shaping weight at the previous detection moment. The following is explained from the time domain and frequency domain respectively.

[0107] The most direct way to reflect the severity of channel changes is to observe the autocorrelation of the channel, that is, the time autocorrelation and frequency autocorrelation. Figure 1 and Figure 2 The channel time domain correlation of the CDL model based on 3GPP 38.901 is illustrated in FIG. Figure 1 Corresponding to the CDL-C channel, Figure 2 Corresponding to the CDL-D channel, Figure 1 and Figure 2It is the channel correlation coefficient when the time interval of a user port is 5ms, 10ms, 15ms and 40ms when the user moves at a speed of 15km / h. Figure 1 and Figure 2 The horizontal axis is frequency and the vertical axis is correlation coefficient. Figure 1 The middle curves 11, 12, 13, and 14 correspond to time intervals of 5ms, 10ms, 15ms, and 40ms, respectively. Figure 2 The middle curves 21, 22, 23, and 24 also correspond to time intervals of 5ms, 10ms, 15ms, and 40ms, respectively. Figure 2 It can be seen that the channel correlation coefficients of the CDL-D channel at different time intervals are mostly above 0.98; Figure 1 It can be seen that in the worst case, most of the correlation coefficients of the CDL-C channel are above 0.4. The above results show that the CDL channel vector can maintain a high time domain correlation within the millisecond time interval.

[0108] like Figure 3 and Figure 4 The channel frequency domain correlation of the CDL model based on 3GPP 38.901 is illustrated in FIG. Figure 3 Corresponding to the CDL-C channel, Figure 4 Corresponding to the CDL-D channel, Figure 3 and Figure 4 It is the correlation coefficient between the channel vectors corresponding to the central frequency point of the CDL channel and the frequency points on the left and right sides within the 100MHz bandwidth at a certain moment. Figure 3 and Figure 4 The horizontal axis is the frequency and the vertical axis is the correlation coefficient. Figure 3 The middle curves 31, 32, 33, and 34 correspond to different ports. Figure 4 The middle curves 41, 42, 43, and 44 also correspond to different ports. Figure 4 It can be seen that the correlation coefficients of the entire frequency band channels of CDL-D are mostly above 0.92; the situation of CDL-C channels is slightly more complicated, but Figure 3 It can be seen that the correlation coefficient can also be maintained above 0.8 in a small frequency range (within 10MHz bandwidth). The above results show that the channel vectors of the CDL channel in a small frequency range (within 10MHz bandwidth) can maintain a high frequency domain correlation.

[0109] Through the above correlation analysis, it can be seen that the EBB shaping weights based on channel estimation maintain strong historical characteristics within the small hourly frequency granularity or small frequency domain granularity. As for the changes in the eigenvalues ​​of the channel in the time domain and frequency domain, the Wielandt-Hoffman theorem and its related corollaries can be used to roughly determine the upper bound of the eigenvalue changes. If the square matrix A is perturbed by the symmetric square matrix E, then:

[0110]

[0111] where λ i (A) represents the i-th eigenvalue of matrix A. The above inequality (1) is the Wielandt-Hoffman theorem, which shows that the sum of the squares of the changes in the eigenvalues ​​of a matrix after perturbation will not exceed the square of the F-norm of the perturbation matrix. The above inequality (1) can be further deduced as follows:

[0112] λ i (A)+λ n (E)≤λ i (A+E)≤λ i (A)+λ1(E) (2)

[0113] Where n is the dimension of the square matrix A. The above inequality (2) gives the upper and lower bounds of the eigenvalues ​​of the perturbed matrix A+E. From the above inequality (32), we can further obtain:

[0114] |λ i (A+E)-λ i (A)|≤max{|λ n (E)|, |λ1(E)|}=‖E‖2≤‖E‖ F (3)

[0115] Equation (3) shows that the absolute value of the change in the eigenvalue of the matrix A+E after perturbation will not exceed the 2-norm of the perturbation matrix.

[0116] Figure 5 and Figure 6 The time domain characteristic values ​​of different ports of the CDL-C channel are shown in FIG. Figure 5 is the channel characteristic value within 600ms in the time domain, Figure 6 for Figure 5 Channel characteristic values ​​within 40ms in the middle time domain 80ms-120ms. Figure 5 and Figure 6 It can be seen that although the channel characteristic value fluctuates frequently over a long period of time, the change of the channel characteristic value is not obvious within a small time domain interval.

[0117] Figure 7 and Figure 8 The frequency domain eigenvalues ​​of different ports of the CDL-C channel are shown in FIG. Figure 6 is the channel characteristic value within the 500MHz bandwidth in the frequency domain, Figure 8 for Figure 7 The characteristic value within the 40MHz bandwidth in the intermediate frequency domain 100MHz-140MHz. Figure 7 and Figure 8It can be seen that although the channel characteristic value fluctuates frequently within the full bandwidth, the change of the channel characteristic value is not obvious within a smaller frequency domain interval.

[0118] From the above correlation analysis of the time domain and frequency domain of the channel, it can be known that for channels with weak time-varying and frequency selectivity, within a smaller time-frequency granularity, its channel characteristic value can be considered to be slowly varying, and there is a higher correlation between the channel characteristic vectors. Therefore, the present application proposes to use the channel characteristic vector at the time of the last beamforming as the initial vector of this EVD operation, so as to achieve the purpose of optimizing the calculation of the EBB shaping weights by making full use of the channel autocorrelation characteristics and improve the calculation efficiency of the beamforming. Based on the above conception, the beamforming method provided by the present application will be described in detail by specific embodiments below. It will be understood that the following several specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0119] Fig. 9 A schematic diagram of a beamforming method provided in an embodiment of the present application. Figure 1 As shown, the method includes:

[0120] S901. Receive an uplink reference signal, and determine an autocorrelation matrix of a channel estimation corresponding to current beamforming according to the uplink reference signal.

[0121] The base station can obtain a channel estimation matrix by performing uplink channel estimation based on the uplink reference signal, and then obtain an autocorrelation matrix of the channel estimation matrix. The channel estimation process in this step is similar to that in the related art and is not described in detail here.

[0122] S902: Determine a channel eigenvector for this beamforming according to an autocorrelation matrix corresponding to this beamforming and a channel eigenvector for a previous beamforming.

[0123] As mentioned in the above introduction, beamforming can be performed in the time domain and the frequency domain, wherein, when beamforming is performed in the time domain, this beamforming is beamforming for an arbitrary time unit, and the previous beamforming is beamforming for the previous time unit of the arbitrary time unit, wherein the time unit can be any one of a transmission time interval (TTI), a frame, a time slot or a symbol. When beamforming is performed in the frequency domain, this 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 the subsequent description of the embodiments of the present application, the time domain or the frequency domain is no longer specifically limited, and only this time and the previous time are used for explanation. It can be understood that it can represent both this time and the previous time in the time domain, and this time and the previous time in the frequency domain.

[0124] Through the aforementioned correlation analysis of the channel in the time domain and frequency domain, it can be known that within a smaller time-frequency granularity, the channel eigenvalue is slowly changing, and there is a high correlation between the channel eigenvectors. Therefore, in the embodiment of the present application, when determining the channel eigenvector of this beamforming, it is not only calculated by the autocorrelation matrix corresponding to this beamforming itself, but is combined with the channel eigenvector of the previous beamforming. For example, in the iterative process of determining the channel eigenvector of this beamforming, the channel eigenvector of the previous beamforming is used as the initial vector of the iteration. Since there is a high correlation between the channel eigenvectors of the two beamformings, the number of iterations can be greatly reduced.

[0125] S903: Perform beamforming on the downlink signal according to the channel eigenvector of the current beamforming.

[0126] After determining the channel characteristic vector of this beamforming, the channel characteristic vector of this beamforming can be normalized, the shaping weight can be determined according to the normalized channel characteristic vector, and the downlink signal can be beamformed according to the shaping weight. The process of beamforming the downlink signal according to the shaping weight in this step is similar to that in the related art and is not described in detail here.

[0127] The beamforming method provided in the embodiment of the present application utilizes the autocorrelation characteristics of the actual channel in the time domain and the frequency domain, and utilizes the channel feature vector of the previous beamforming to calculate the channel feature vector of this beamforming, which can reduce the amount of calculation in the beamforming process and improve efficiency.

[0128] On the basis of the above embodiment, 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 is described in combination with the power method.

[0129] It should be noted that there are n channel eigenvectors for this beamforming, where n is the number of streams for this beamforming, i.e., the dimension of the autocorrelation matrix, and n is a positive integer. When the power method is used to determine the channel eigenvector for this beamforming, the channel eigenvector of the previous beamforming is used as the initial vector in the power method, that is,

[0130] For the i-th channel eigenvector of this beamforming, the i-th channel eigenvector of the previous beamforming is used as the initial vector, and the value of i traverses all integers between 1 and n; the vector obtained by multiplying the initial vector by the autocorrelation matrix corresponding to this beamforming is used as the new initial vector, and this step is repeated until the first preset number of times is reached, and the obtained vector is determined as the i-th channel eigenvector of this beamforming; the i-th channel eigenvalue of this beamforming is determined according to the i-th channel eigenvector of this beamforming and the autocorrelation matrix corresponding to this beamforming, and the autocorrelation matrix corresponding to this beamforming is updated according to the i-th channel eigenvalue of this beamforming and the i-th channel eigenvector of this beamforming, and the updated autocorrelation matrix corresponding to this beamforming is used to determine the i+1-th channel eigenvector of this beamforming; by executing the above steps, the n channel eigenvectors of this beamforming can be determined.

[0131] The example algorithm is as follows:

[0132]

[0133]

[0134] Among them, the superscript (t) represents an arbitrary time unit, and the superscript (t-1) represents the time unit before the arbitrary time unit. The above algorithm only takes the time domain as an example. When calculating in the frequency domain, it is only necessary to modify the superscript representing the arbitrary time unit to the superscript representing the specified frequency point, and modify the superscript representing the time unit before the arbitrary time unit to the superscript representing the previous adjacent frequency point of the specified frequency point.

[0135] Among them, R (t) is the autocorrelation matrix of time unit t, represents the i-th converged channel eigenvector of time unit t-1, represents the i-th converged channel eigenvector of time unit t, Represents the ith channel eigenvalue of time unit t. In the above algorithm, each time the channel eigenvector is calculated, the channel eigenvector of the previous moment is used as the initial vector of the iteration, so that the setting of the number of iterations N can be reduced. Since most of the calculations are performed in the iterative loop, the number of iterations can be used as an indicator to compare the complexity before and after the algorithm is improved. Simulations show that, without losing the shaping performance, the total number of iterations after improvement under the CDL-D channel is about 17.77% of the original number, and the total number of iterations after improvement under the CDL-C channel is about 36.29% of the original number, which greatly reduces the number of algorithm iterations, accelerates the convergence speed of the algorithm, and improves efficiency.

[0136] In addition to the above-mentioned improvement to the power method, another method for calculating the channel eigenvector may be provided in the embodiment of the present application. 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 channel eigenvalue of the previous beamforming.

[0137] In this case, the inverse iteration method can be used for iterative calculation. As mentioned above, the changes in the channel eigenvalues ​​after two beamforming are often not very large. For this problem of obtaining the eigenvector when the eigenvalue approximation is known, the inverse iteration method is particularly suitable. The advantage of using the inverse iteration method is that it converges quickly and requires fewer iterations than the power method. The process of using this method is as follows:

[0138] For the i-th channel eigenvector of this beamforming, the i-th channel eigenvector of the previous beamforming is used as the initial vector, and the i-th channel eigenvalue of the previous beamforming is used as the initial eigenvalue, where the value of i traverses all integers between 1 and n, where n is the number of streams of this beamforming, and n is a positive integer; the inverse matrix is ​​determined according to the autocorrelation matrix and the initial eigenvalue corresponding to this beamforming; the vector obtained by multiplying the initial vector by the inverse matrix is ​​used as the new initial vector, and this step is repeated until the second preset number of times is reached, and the obtained vector is determined as the i-th channel eigenvector of this beamforming, and the i-th channel eigenvalue of this beamforming is determined according to the i-th channel eigenvector of this beamforming and the autocorrelation matrix corresponding to this beamforming; by executing the above steps, the n channel eigenvectors of this beamforming can be determined.

[0139] The example algorithm is as follows:

[0140]

[0141] Similar to the above, in this algorithm, the superscript (t) represents an arbitrary time unit, and the superscript (t-1) represents the previous time unit of the arbitrary time unit. The above algorithm only takes the time domain as an example. When calculating in the frequency domain, you only need to modify the superscript representing the arbitrary time unit to the superscript representing the specified frequency point, and modify the superscript representing the previous time unit of the arbitrary time unit to the superscript representing the previous adjacent frequency point of the specified frequency point.

[0142] Among them, R (t) is the autocorrelation matrix of time unit t, I is the identity matrix, is the inverse matrix of time unit t, represents the i-th converged channel eigenvector of time unit t-1, represents the ith channel eigenvalue of time unit t-1, represents the i-th converged channel eigenvector of time unit t, Indicates the ith channel eigenvalue of time unit t. In the above algorithm, each time the channel eigenvector is calculated, the channel eigenvector of the previous time unit is used as the initial vector of the iteration, and the channel eigenvalue of the previous time unit is used as the initial eigenvalue of the iteration, so that the setting of the number of iterations N can be further reduced. For example, the number of iterations N can be set to 4 after simulation verification. The normalization operation can be performed inside or outside the iteration process. For example, in the above example, the normalization operation can be performed outside the iteration process, which can reduce the number of modulo operations, reduce the amount of calculation, and improve efficiency.

[0143] The above algorithm calculates the matrix When the inversion object is a Hermitian positive definite symmetric matrix, assuming that the matrix dimension is 4×4, a fast matrix inversion algorithm based on Cholesky decomposition can be used. The implementation of Cholesky decomposition can be completed using the block method in 4 operations. Each operation includes calculating a column of the lower triangular matrix obtained by Cholesky decomposition and updating some elements of the original matrix. The inversion of the obtained lower triangular matrix can also be completed in 4 steps, and each operation calculates a diagonal of the lower triangular inverse matrix.

[0144] The above embodiment introduces the use of an improved power method or an inverse iteration method to calculate the channel eigenvector. In practical applications, the base station can select one of them for calculation according to the actual situation. When the channel mutates, such as multipath birth and death in a time-varying channel, LOS and NLOS scene switching, etc. (or large frequency selectivity in the frequency domain), the channel eigenvalues ​​of different time units (or frequency points) are prone to mutation. At this time, the effects of the initial eigenvalues ​​and eigenvectors have failed, which may cause the inverse iteration method to converge to the same eigenvector or slow down the convergence speed. At this time, the power method can be used for calculation. This situation can be predicted using the corollary of the Wielandt-Hoffman theorem introduced above, that is, determine the difference matrix of the autocorrelation matrix of the channel estimate corresponding to this beamforming and the autocorrelation matrix of the channel estimate corresponding to the previous beamforming; if the 2-norm of the difference matrix is ​​less than or equal to the preset value, the channel eigenvector of this beamforming is determined according to the autocorrelation matrix corresponding to this beamforming, as well as the channel eigenvector and channel eigenvalue of the previous beamforming.

[0145] The above-mentioned inequality (3) shows that if the square matrix A is perturbed by the symmetric square matrix E, the absolute value of the change in the eigenvalue of the matrix A+E after the perturbation will not exceed the 2-norm of the perturbation matrix. Applied to the beamforming scenario of the embodiment of the present application, the square matrix A is the autocorrelation matrix of the channel estimation corresponding to the previous beamforming, and the matrix A+E is the autocorrelation matrix of the channel estimation corresponding to the current beamforming. The upper limit of the change in the eigenvalue of the matrix A+E and the square matrix A is the 2-norm of the difference matrix E between the two. When the 2-norm of the difference matrix is ​​less than or equal to the preset value, it can be determined that the eigenvalue of this beamforming and the previous beamforming do not change much. At this time, the inverse iteration method can be used. When the 2-norm of the difference matrix is ​​greater than the preset value, it can be determined that the eigenvalue of this beamforming and the previous beamforming change greatly. At this time, the power method can be used. The size of the preset value can be set according to the actual situation of the channel.

[0146] Fig.10 A schematic diagram of a beamforming device provided in an embodiment of the present application Figure 1 .like Fig.10 As shown, the beamforming device includes: a memory 101, a transceiver 102, and a processor 103.

[0147] The memory 101 is used to store computer programs.

[0148] The transceiver 102 is used to send and receive data under the control of the processor.

[0149] The processor 103 is configured to read the computer program stored in the memory and perform the following operations:

[0150] receiving an uplink reference signal, and determining an autocorrelation matrix of a channel estimation corresponding to the current beamforming according to the uplink reference signal;

[0151] 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;

[0152] The downlink signal is beamformed according to the channel eigenvector of this beamforming.

[0153] In one implementation, determining a channel eigenvector for current beamforming according to an autocorrelation matrix corresponding to current beamforming and a channel eigenvector for previous beamforming includes:

[0154] For the i-th channel eigenvector of this beamforming, the i-th channel eigenvector of the previous beamforming is used as the initial vector, where the value of i traverses all integers between 1 and n, n is the number of streams of this beamforming, and n is a positive integer;

[0155] The vector obtained by multiplying the initial vector by the autocorrelation matrix corresponding to the current beamforming is used as a new initial vector, and this step is repeated until a first preset number of times is reached, and the obtained vector is determined as the i-th channel eigenvector of the current beamforming;

[0156] Determine the i-th channel eigenvalue of this beamforming according to the i-th channel eigenvector of this beamforming and the autocorrelation matrix corresponding to this beamforming, and update the autocorrelation matrix corresponding to this beamforming according to the i-th channel eigenvalue of this beamforming and the i-th channel eigenvector of this beamforming. The updated autocorrelation matrix is ​​used to determine the i+1-th channel eigenvector of this beamforming.

[0157] Execute the above steps to determine the channel eigenvector of this beamforming.

[0158] In one implementation, determining a channel eigenvector for current beamforming according to an autocorrelation matrix corresponding to current beamforming and a channel eigenvector for previous beamforming includes:

[0159] The channel eigenvector of the current beamforming is determined according to the autocorrelation matrix corresponding to the current beamforming, and the channel eigenvector and channel eigenvalue of the previous beamforming.

[0160] In one implementation, determining a channel eigenvector for the current beamforming according to an autocorrelation matrix corresponding to the current beamforming and a channel eigenvector and a channel eigenvalue of the previous beamforming includes:

[0161] For the ith channel eigenvector of this beamforming, the ith channel eigenvector of the previous beamforming is used as the initial vector, and the ith channel eigenvalue of the previous beamforming is used as the initial eigenvalue, where the value of i traverses all integers between 1 and n, and the value of i ranges from 1 to n, and n is the number of streams of this beamforming, and n is a positive integer;

[0162] Determine the inverse matrix according to the autocorrelation matrix and initial eigenvalues ​​corresponding to this beamforming;

[0163] The vector obtained by multiplying the initial vector by the inverse matrix is ​​used as a new initial vector, and this step is repeated until a second preset number of times is reached, and the obtained vector is determined as the i-th channel eigenvector of this beamforming, and the i-th channel eigenvalue of this beamforming is determined according to the i-th channel eigenvector of this beamforming and the autocorrelation matrix corresponding to this beamforming;

[0164] Execute the above steps to determine the channel eigenvector of this beamforming.

[0165] In one implementation, determining a channel eigenvector for the current beamforming according to an autocorrelation matrix corresponding to the current beamforming and a channel eigenvector and a channel eigenvalue of the previous beamforming includes:

[0166] Determine a difference matrix between an autocorrelation matrix of a channel estimate corresponding to the current beamforming and an autocorrelation matrix of a channel estimate corresponding to the previous beamforming;

[0167] When the 2-norm of the difference matrix is ​​less than or equal to a preset value, the channel eigenvector of this beamforming is determined according to the autocorrelation matrix corresponding to this beamforming, and the channel eigenvector and channel eigenvalue of the previous beamforming.

[0168] In one implementation, the current beamforming is beamforming for an arbitrary time unit, and the previous beamforming is beamforming for a time unit before the arbitrary time unit.

[0169] In one implementation, the current beamforming is beamforming for a designated frequency point, and the previous beamforming is beamforming for an adjacent frequency point that is separated from the designated frequency point by a frequency domain beamforming granularity.

[0170] In one implementation, beamforming is performed on a downlink signal according to a channel eigenvector of current beamforming, including:

[0171] The channel characteristic vector of this beamforming is normalized, a beamforming weight is determined according to the normalized channel characteristic vector, and beamforming is performed on the downlink signal according to the beamforming weight.

[0172] It should be noted here that the above-mentioned device provided in the present application can implement all the method steps in the above-mentioned method embodiment and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as the method embodiment will not be described in detail here.

[0173] Fig.11 A schematic diagram of a beamforming device provided in an embodiment of the present application Figure 2 .like Fig.11 As shown, the beamforming device includes:

[0174] The receiving unit 111 is 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;

[0175] A determination unit 112, configured to determine a channel eigenvector for this beamforming according to an autocorrelation matrix corresponding to this beamforming and a channel eigenvector for a previous beamforming;

[0176] The beamforming unit 113 is configured to perform beamforming on the downlink signal according to the channel characteristic vector of the current beamforming.

[0177] In one implementation, the determining unit 112 is configured to:

[0178] For the i-th channel eigenvector of this beamforming, the i-th channel eigenvector of the previous beamforming is used as the initial vector, where the value of i traverses all integers between 1 and n, n is the number of streams of this beamforming, and n is a positive integer;

[0179] The vector obtained by multiplying the initial vector by the autocorrelation matrix corresponding to the current beamforming is used as a new initial vector, and this step is repeated until a first preset number of times is reached, and the obtained vector is determined as the i-th channel eigenvector of the current beamforming;

[0180] Determine the i-th channel eigenvalue of this beamforming according to the i-th channel eigenvector of this beamforming and the autocorrelation matrix corresponding to this beamforming, and update the autocorrelation matrix corresponding to this beamforming according to the i-th channel eigenvalue of this beamforming and the i-th channel eigenvector of this beamforming. The updated autocorrelation matrix is ​​used to determine the i+1-th channel eigenvector of this beamforming.

[0181] Execute the above steps to determine the channel eigenvector of this beamforming.

[0182] In one implementation, the determining unit 112 is configured to:

[0183] The channel eigenvector of the current beamforming is determined according to the autocorrelation matrix corresponding to the current beamforming, and the channel eigenvector and channel eigenvalue of the previous beamforming.

[0184] In one implementation, the determining unit 112 is configured to:

[0185] For the i-th channel eigenvector of this beamforming, the i-th channel eigenvector of the previous beamforming is used as the initial vector, and the i-th channel eigenvalue of the previous beamforming is used as the initial eigenvalue, where the value of i traverses all integers between 1 and n, n is the number of streams of this beamforming, and n is a positive integer;

[0186] Determine the inverse matrix according to the autocorrelation matrix and initial eigenvalues ​​corresponding to this beamforming;

[0187] The vector obtained by multiplying the initial vector by the inverse matrix is ​​used as a new initial vector, and this step is repeated until a second preset number of times is reached, and the obtained vector is determined as the i-th channel eigenvector of this beamforming, and the i-th channel eigenvalue of this beamforming is determined according to the i-th channel eigenvector of this beamforming and the autocorrelation matrix corresponding to this beamforming;

[0188] Execute the above steps to determine the channel eigenvector of this beamforming.

[0189] In one implementation, the determining unit 112 is configured to:

[0190] Determine a difference matrix between an autocorrelation matrix of a channel estimate corresponding to the current beamforming and an autocorrelation matrix of a channel estimate corresponding to the previous beamforming;

[0191] When the 2-norm of the difference matrix is ​​less than or equal to a preset value, the channel eigenvector of this beamforming is determined according to the autocorrelation matrix corresponding to this beamforming, and the channel eigenvector and channel eigenvalue of the previous beamforming.

[0192] In one implementation, the current beamforming is beamforming for an arbitrary time unit, and the previous beamforming is beamforming for a time unit before the arbitrary time unit.

[0193] In one implementation, the current beamforming is beamforming for a designated frequency point, and the previous beamforming is beamforming for an adjacent frequency point that is separated from the designated frequency point by a frequency domain beamforming granularity.

[0194] In one embodiment, the shaping unit 113 is used to:

[0195] The channel characteristic vector of this beamforming is normalized, a beamforming weight is determined according to the normalized channel characteristic vector, and beamforming is performed on the downlink signal according to the beamforming weight.

[0196] It should be noted here that the above-mentioned device provided in the present application can implement all the method steps in the above-mentioned method embodiment and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as the method embodiment will not be described in detail here.

[0197] It should be noted that the division of units in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional unit in each embodiment of the present application may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0198] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a processor-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) or a processor (processor) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0199] An embodiment of the present application further provides a processor-readable storage medium, wherein the processor-readable storage medium stores a computer program, and the computer program is used to enable a processor to execute the beamforming method in any possible implementation manner described above.

[0200] An embodiment of the present application further provides a computer program product, including a computer program, which implements the beamforming method in any possible implementation manner when executed by a processor.

[0201] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0202] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer executable instructions. These computer executable instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0203] These processor executable instructions may also be stored in a processor readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the processor readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0204] These processor-executable instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0205] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A beamforming method, characterized in that: The method comprises: receiving an uplink reference signal, and determining an autocorrelation matrix of a channel estimation corresponding to the current beamforming according to the uplink reference signal; 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; the current beamforming is beamforming for an arbitrary time unit, and the previous beamforming is beamforming for a time unit before the arbitrary time unit; or, the current beamforming is beamforming for a designated frequency point, and the previous beamforming is beamforming for an adjacent frequency point separated from the designated frequency point by a frequency domain beamforming granularity; The channel characteristic vector of this beamforming is normalized, a beamforming weight is determined according to the normalized channel characteristic vector, and beamforming is performed on the downlink signal according to the beamforming weight.

2. The method according to claim 1, characterized in that 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: For the i-th channel eigenvector of the current beamforming, the i-th channel eigenvector of the previous beamforming is used as the initial vector, wherein the value of i traverses all integers between 1 and n, n is the number of streams of the current beamforming, and n is a positive integer; A vector obtained by multiplying the initial vector by the autocorrelation matrix corresponding to the current beamforming is used as a new initial vector, and this step is repeated until a first preset number of times is reached, and the obtained vector is determined as the i-th channel eigenvector of the current beamforming; Determine the i-th channel eigenvalue of the current beamforming according to the i-th channel eigenvector of the current beamforming and the autocorrelation matrix corresponding to the current beamforming, and update the autocorrelation matrix corresponding to the current beamforming according to the i-th channel eigenvalue of the current beamforming and the i-th channel eigenvector of the current beamforming, and the updated autocorrelation matrix is ​​used to determine the i+1-th channel eigenvector of the current beamforming; Repeat the above steps to determine the channel eigenvector of this beamforming.

3. The method according to claim 1, characterized in that 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: For the i-th channel eigenvector of this beamforming, the i-th channel eigenvector of the previous beamforming is used as the initial vector, and the i-th channel eigenvalue of the previous beamforming is used as the initial eigenvalue, wherein the value of i traverses all integers between 1 and n, and n is the number of streams of this beamforming, and n is a positive integer; Determine an inverse matrix according to the autocorrelation matrix corresponding to the current beamforming and the initial eigenvalue; A vector obtained by multiplying the initial vector by the inverse matrix is ​​used as a new initial vector, and this step is repeated until a second preset number of times is reached, and the obtained vector is determined as the i-th channel eigenvector of this beamforming, and the i-th channel eigenvalue of this beamforming is determined according to the i-th channel eigenvector of this beamforming and the autocorrelation matrix corresponding to this beamforming; Repeat the above steps to determine the channel eigenvector of this beamforming.

4. The method according to claim 1, characterized in that: 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: Determine a difference matrix between 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; When the 2-norm of the difference matrix is ​​less than or equal to a preset value, the channel eigenvector of this beamforming is determined according to the autocorrelation matrix corresponding to this beamforming, and the channel eigenvector and channel eigenvalue of the previous beamforming.

5. A beamforming device, characterized in that: The device comprises: a memory, a transceiver, and a processor; The memory is used to store computer programs; The transceiver is used to send and receive data under the control of the processor; The processor is configured to read the computer program stored in the memory and perform the following operations: receiving an uplink reference signal, and determining an autocorrelation matrix of a channel estimation corresponding to the current beamforming according to the uplink reference signal; 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; the current beamforming is beamforming for an arbitrary time unit, and the previous beamforming is beamforming for a time unit before the arbitrary time unit; or, the current beamforming is beamforming for a designated frequency point, and the previous beamforming is beamforming for an adjacent frequency point separated from the designated frequency point by a frequency domain beamforming granularity; The channel characteristic vector of this beamforming is normalized, a beamforming weight is determined according to the normalized channel characteristic vector, and beamforming is performed on the downlink signal according to the beamforming weight.

6. The device according to claim 5, characterized in that 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: For the i-th channel eigenvector of the current beamforming, the i-th channel eigenvector of the previous beamforming is used as the initial vector, wherein the value of i traverses all integers between 1 and n, n is the number of streams of the current beamforming, and n is a positive integer; A vector obtained by multiplying the initial vector by the autocorrelation matrix corresponding to the current beamforming is used as a new initial vector, and this step is repeated until a first preset number of times is reached, and the obtained vector is determined as the i-th channel eigenvector of the current beamforming; Determine the i-th channel eigenvalue of the current beamforming according to the i-th channel eigenvector of the current beamforming and the autocorrelation matrix corresponding to the current beamforming, and update the autocorrelation matrix corresponding to the current beamforming according to the i-th channel eigenvalue of the current beamforming and the i-th channel eigenvector of the current beamforming, and the updated autocorrelation matrix is ​​used to determine the i+1-th channel eigenvector of the current beamforming; Repeat the above steps to determine the channel eigenvector of this beamforming.

7. The device according to claim 5, characterized in that 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: For the i-th channel eigenvector of this beamforming, the i-th channel eigenvector of the previous beamforming is used as the initial vector, and the i-th channel eigenvalue of the previous beamforming is used as the initial eigenvalue, wherein the value of i traverses all integers between 1 and n, n is the number of streams of this beamforming, and n is a positive integer; Determine an inverse matrix according to the autocorrelation matrix corresponding to the current beamforming and the initial eigenvalue; A vector obtained by multiplying the initial vector by the inverse matrix is ​​used as a new initial vector, and this step is repeated until a second preset number of times is reached, and the obtained vector is determined as the i-th channel eigenvector of this beamforming, and the i-th channel eigenvalue of this beamforming is determined according to the i-th channel eigenvector of this beamforming and the autocorrelation matrix corresponding to this beamforming; Repeat the above steps to determine the channel eigenvector of this beamforming.

8. The device according to claim 5, characterized in that 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: Determine a difference matrix between 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; When the 2-norm of the difference matrix is ​​less than or equal to a preset value, the channel eigenvector of this beamforming is determined according to the autocorrelation matrix corresponding to this beamforming, and the channel eigenvector and channel eigenvalue of the previous beamforming.

9. A beamforming device, characterized in that: The device comprises: A receiving unit, 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 determination unit, configured to determine a channel eigenvector of this beamforming according to the autocorrelation matrix corresponding to this beamforming and a channel eigenvector of a previous beamforming; the current beamforming is beamforming for an arbitrary time unit, and the previous beamforming is beamforming for a time unit before the arbitrary time unit; or, the current beamforming is beamforming for a designated frequency point, and the previous beamforming is beamforming for an adjacent frequency point separated from the designated frequency point by a frequency domain shaping granularity; The shaping unit is used to normalize the channel characteristic vector of this beamforming, determine the shaping weight according to the normalized channel characteristic vector, and perform beamforming on the downlink signal according to the shaping weight.

10. The device according to claim 9, characterized in that The determining unit is used for: For the i-th channel eigenvector of the current beamforming, the i-th channel eigenvector of the previous beamforming is used as the initial vector, wherein the value of i traverses all integers between 1 and n, n is the number of streams of the current beamforming, and n is a positive integer; A vector obtained by multiplying the initial vector by the autocorrelation matrix corresponding to the current beamforming is used as a new initial vector, and this step is repeated until a first preset number of times is reached, and the obtained vector is determined as the i-th channel eigenvector of the current beamforming; Determine the i-th channel eigenvalue of the current beamforming according to the i-th channel eigenvector of the current beamforming and the autocorrelation matrix corresponding to the current beamforming, and update the autocorrelation matrix corresponding to the current beamforming according to the i-th channel eigenvalue of the current beamforming and the i-th channel eigenvector of the current beamforming, and the updated autocorrelation matrix is ​​used to determine the i+1-th channel eigenvector of the current beamforming; Repeat the above steps to determine the channel eigenvector of this beamforming.

11. The device according to claim 9, characterized in that The determining unit is used for: For the i-th channel eigenvector of this beamforming, the i-th channel eigenvector of the previous beamforming is used as the initial vector, and the i-th channel eigenvalue of the previous beamforming is used as the initial eigenvalue, wherein the value of i traverses all integers between 1 and n, and n is the number of streams of this beamforming, and n is a positive integer; Determine an inverse matrix according to the autocorrelation matrix corresponding to the current beamforming and the initial eigenvalue; A vector obtained by multiplying the initial vector by the inverse matrix is ​​used as a new initial vector, and this step is repeated until a second preset number of times is reached, and the obtained vector is determined as the i-th channel eigenvector of this beamforming, and the i-th channel eigenvalue of this beamforming is determined according to the i-th channel eigenvector of this beamforming and the autocorrelation matrix corresponding to this beamforming; Repeat the above steps to determine the channel eigenvector of this beamforming.

12. The device according to claim 9, characterized in that The determining unit is used for: Determine a difference matrix between 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; When the 2-norm of the difference matrix is ​​less than or equal to a preset value, the channel eigenvector of this beamforming is determined according to the autocorrelation matrix corresponding to this beamforming, and the channel eigenvector and channel eigenvalue of the previous beamforming.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is used to make a computer execute the method according to any one of claims 1 to 4.

14. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a computer or a processor, the method according to any one of claims 1 to 4 is implemented.

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