A beamforming method, electronic device and storage medium

Abstract

This application provides a beamforming method, electronic device, and storage medium, relating to the field of communication technology. Based on the number of information streams used for information transmission, a singular value decomposition (SVD) is performed on an acquired initial channel matrix to obtain the SVD result, which includes a singular value matrix and a first singular vector. Based on set power allocation coefficients and the singular value matrix, a power allocation coefficient matrix is ​​determined. Based on the power allocation coefficient matrix and the first singular vector, beamforming is applied to the information streams used for information transmission. By redistributing the power of the information in the information streams, the difference in the first singular vectors corresponding to the information in each information stream can be reduced, thereby decreasing inter-stream interference between the information streams.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a beamforming method, electronic device, and storage medium. Background Technology

[0002] In 5G Multiple-Input Multiple-Output (MIMO) communication scenarios, especially in applications where the base station signal source transmits multiple information streams for multi-stream transmission, to achieve better data transmission performance, the information transmitted by each stream is beamformed and mapped onto the transmitting antenna array. The signals are then transmitted through channels to the terminal receiving antenna array. Multi-stream transmission refers to the simultaneous transmission of multiple sets of information via multiple streams at the same frequency, with each stream transmitting one set of information.

[0003] Existing beamforming methods often introduce significant inter-stream interference during beamforming, which leads to large performance differences among the various information streams and makes it impossible to guarantee balanced performance across them. Summary of the Invention

[0004] To address the problems in the prior art, embodiments of this application provide a beamforming method, an electronic device, and a storage medium that can reduce inter-stream interference between various information streams.

[0005] In a first aspect, embodiments of this application provide a beamforming method, the method comprising:

[0006] Based on the amount of information streams used for information transmission, singular value decomposition is performed on the acquired initial channel matrix to obtain the singular value decomposition result; the singular value decomposition result includes a singular value matrix and a first singular vector;

[0007] Based on the set power allocation coefficients and the singular value matrix, the power allocation coefficient matrix is ​​determined;

[0008] Based on the power allocation coefficient matrix and the first singular vector, the beam of the information stream used for transmitting information is shaped.

[0009] This application provides a beamforming method that performs singular value decomposition on the acquired initial channel matrix and processes the first singular vector in the obtained singular value decomposition result using a set power coefficient to obtain a power allocation coefficient matrix. Then, the power allocation coefficient matrix and the first singular vector are used to shape the beam of the information stream used for transmitting information, so as to realize the redistribution of power of each information stream, which can reduce the difference of the first singular vectors corresponding to each information stream and reduce inter-stream interference between each information stream.

[0010] In one possible implementation, the dimension of the initial channel matrix is ​​determined based on the number of transmitting antennas and the number of receiving antennas used for transmitting information; the initial channel matrix is ​​used to characterize the information transmission coefficients of the channel from each of the transmitting antennas to each of the receiving antennas; the number of rows and columns of the singular value matrix are both the number of information streams.

[0011] In one possible implementation, the singular value decomposition result further includes a second singular vector; the step of performing singular value decomposition on the acquired initial channel matrix to obtain the singular value decomposition result includes:

[0012] The initial channel matrix is ​​decomposed using the following formula:

[0013] H=UΣV H

[0014] Wherein, H represents the initial channel matrix; U represents the second singular vector; Σ represents the singular value matrix; V represents the first singular vector; V H Let represent the conjugate transpose of the first singular vector.

[0015] In one possible implementation, the beamforming of the information stream used for transmitting information based on the power allocation coefficient matrix and the first singular vector includes:

[0016] Power allocation is performed on the first singular vector according to the power allocation coefficient matrix to determine the allocation result matrix;

[0017] Based on the allocation result matrix, the beamforming factor is determined;

[0018] Based on the beamforming factor, the beam used to transmit information is shaped.

[0019] In the above method, when the receiver has different inter-stream interference suppression capabilities for each information stream, the order in which the information from each information stream participates in the calculation can be changed by reordering, so that the magnitude of the inter-stream interference for each information stream matches the receiver's interference suppression capability for each information stream.

[0020] In one possible implementation, the power allocation coefficient is a power allocation coefficient matrix; the step of allocating power to the first singular vector based on the power allocation coefficient matrix to determine the allocation result matrix includes:

[0021] The allocation result matrix is ​​determined using the following formula:

[0022] V PA =VΦ

[0023] Among them, VPA Φ represents the allocation result matrix; Φ represents the power allocation coefficient matrix; V represents the first singular vector.

[0024] In one possible implementation, determining the beamforming factor based on the allocation result matrix includes:

[0025] The allocation result matrix is ​​reordered based on the rearrangement matrix to determine the reordered allocation result matrix corresponding to the allocation result matrix; each column of the allocation result matrix represents a vector for power allocation of the information contained in the beam of each information stream.

[0026] The reordering and allocation result matrix is ​​subjected to geometric mean decomposition transformation to determine the beamforming factor.

[0027] In one possible implementation, the step of reordering each column of the allocation result matrix based on the rearrangement matrix to determine the reordered allocation result matrix corresponding to the allocation result matrix includes:

[0028] The reordering allocation result matrix corresponding to the allocation result matrix is ​​determined using the following formula:

[0029] V PA ′ =V PA ψ

[0030] Among them, V PA ′ V represents the reordering and allocation result matrix; ψ represents the reordering matrix; V PA This represents the allocation result matrix.

[0031] In one possible implementation, the number of rows and columns of the rearrangement matrix are both equal to the number of information streams.

[0032] In one possible implementation, shaping the beam for transmitting information based on a determined beamforming factor includes:

[0033] The initial channel matrix is ​​reordered based on the rearrangement matrix to determine the reordered channel matrix;

[0034] The beamforming factor is used to beamform each column vector in the reordered channel matrix; wherein each column vector in the reordered channel matrix represents a beam of the information stream.

[0035] Secondly, embodiments of this application provide an electronic device, the electronic device comprising:

[0036] The decomposition unit is used to perform singular value decomposition on the acquired initial channel matrix based on the number of information streams used for transmitting information, and obtain the singular value decomposition result;

[0037] The allocation unit is used to determine the power allocation coefficient matrix based on the set power allocation coefficients and the singular value matrix;

[0038] A beamforming unit is used to shape the beam of the information stream used for transmitting information based on the power allocation coefficient matrix and the first singular vector.

[0039] In one possible implementation, the decomposition unit is further configured to:

[0040] The initial channel matrix is ​​decomposed using the following formula:

[0041] H=UΣV H

[0042] Wherein, H represents the initial channel matrix; U represents the second singular vector; Σ represents the singular value matrix; V represents the first singular vector; V H Let represent the conjugate transpose of the first singular vector.

[0043] In one possible implementation, the beamforming unit is further configured to:

[0044] Power allocation is performed on the first singular vector according to the power allocation coefficient matrix to determine the allocation result matrix;

[0045] Based on the allocation result matrix, the beamforming factor is determined;

[0046] Based on the beamforming factor, the beam used to transmit information is shaped.

[0047] In one possible implementation, the beamforming unit is further configured to:

[0048] The allocation result matrix is ​​determined using the following formula:

[0049] V PA =VΦ

[0050] Among them, V PA Φ represents the allocation result matrix; Φ represents the power allocation coefficient matrix; V represents the first singular vector.

[0051] In one possible implementation, the beamforming unit is further configured to:

[0052] The allocation result matrix is ​​reordered based on the rearrangement matrix to determine the reordered allocation result matrix corresponding to the allocation result matrix; each column of the allocation result matrix represents a vector for power allocation of the information contained in the beam of each information stream.

[0053] The reordering and allocation result matrix is ​​subjected to geometric mean decomposition transformation to determine the beamforming factor.

[0054] In one possible implementation, the beamforming unit is further configured to:

[0055] The reordering allocation result matrix corresponding to the allocation result matrix is ​​determined using the following formula:

[0056] V PA ′ =V PA ψ

[0057] Among them, V PA ′ V represents the reordering and allocation result matrix; ψ represents the reordering matrix; V PA This represents the allocation result matrix.

[0058] In one possible implementation, the beamforming unit is further configured to:

[0059] The initial channel matrix is ​​reordered based on the rearrangement matrix to determine the reordered channel matrix;

[0060] The beamforming factor is used to beamform each column vector in the reordered channel matrix; wherein each column vector in the reordered channel matrix represents a beam of the information stream.

[0061] Thirdly, embodiments of this application provide an electronic device, including: a memory, a transceiver, and a processor; the memory is used to store a computer program; the transceiver is used to transmit and receive data under the control of the processor; the processor reads the computer program in the memory and executes the method described in any one of the beamforming methods in the first aspect.

[0062] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any one of the beamforming methods in the first aspect.

[0063] Fifthly, embodiments of this application provide a computer program product including computer-executable instructions, which are used to cause a computer to perform a method as described in any one of the beamforming methods in the first aspect.

[0064] The technical effects achieved by the second to fifth aspects provided in the embodiments of this application are the same as the technical effects of the beamforming method provided in the first aspect, and will not be repeated here. Attached Figure Description

[0065] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0066] Figure 1 This is a schematic diagram illustrating an application scenario of a beamforming method provided in an embodiment of this application.

[0067] Figure 2 This is a schematic diagram of a downlink transmission process provided in an embodiment of this application;

[0068] Figure 3 A schematic flowchart of a beamforming method provided in an embodiment of this application;

[0069] Figure 4 A flowchart illustrating another beamforming method provided in an embodiment of this application;

[0070] Figure 5 A comparative schematic diagram of a beamforming amplitude gain matrix provided for an embodiment of this application;

[0071] Figure 6 A comparative schematic diagram of another beamforming amplitude gain matrix provided in the embodiments of this application;

[0072] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application;

[0073] Figure 8 This is a schematic diagram of the structure of another electronic device provided in an embodiment of this application. Detailed Implementation

[0074] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0075] It should be noted that the terms "comprising" and "having" and their variations used in this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or device.

[0076] To address the issue of significant inter-flow interference in existing beamforming methods, this application provides a beamforming method that, based on the number of information flows used for transmission, performs singular value decomposition (SVD) on an acquired initial channel matrix to obtain the SVD result. The SVD result includes a singular value matrix and a first singular vector. Based on set power allocation coefficients and the singular value matrix, a power allocation coefficient matrix is ​​determined. Then, based on the power allocation coefficient matrix and the first singular vector, beamforming is applied to the information flows used for transmission. By redistributing the power of information within the information flows, the difference in the first singular vectors corresponding to the information in each flow can be reduced, thereby decreasing inter-flow interference.

[0077] Figure 1 This application illustrates a specific application scenario of a beamforming method provided by an embodiment of this application, such as... Figure 1 As shown, this application scenario includes a base station 11 and a terminal device 12. The base station 11 and the terminal device 12 can connect wirelessly and exchange data.

[0078] Among them, the terminal device 12 can be a mobile phone, computer, etc. Figure 1 Taking a mobile phone as an example, the beamforming method provided in this application embodiment can be executed by an electronic device in a base station 11. Taking execution by an electronic device in a base station 11 as an example, during downlink channel transmission, the electronic device in the base station 11 performs singular value decomposition on the acquired initial channel matrix based on the number of information streams used for information transmission, and obtains the singular value decomposition result. The singular value decomposition result includes a singular value matrix and a first singular vector. Based on the set power allocation coefficient and the singular value matrix, a power allocation coefficient matrix is ​​determined. Based on the power allocation coefficient matrix and the first singular vector, beamforming is performed on the information streams used for information transmission.

[0079] To better understand the technical solutions provided in the embodiments of this application, the following is a brief introduction to the application scenarios to which the technical solutions provided in the embodiments of this application are applicable. It should be noted that the application scenarios described below are only for illustrating the embodiments of this application and are not intended to limit the scope. In specific implementation, the technical solutions provided in the embodiments of this application can be flexibly applied according to actual needs.

[0080] Figure 2A schematic diagram of a beamforming and information transmission process via a downlink channel is shown, as follows: Figure 2 As shown. During downlink transmission, the information source of base station 11 includes multiple information streams. Each information stream contains information that needs to be transmitted to terminal device 12. The information in each information stream is mapped onto the transmitting antenna array through beamforming. The information in the information stream transmitted by each transmitting antenna reaches the receiving antenna array of the terminal device through the channel.

[0081] Figure 3 This illustration shows a flowchart of a beamforming method provided in an embodiment of this application. This beamforming method can be applied to electronic devices in base station 11, such as... Figure 3 As shown, the beamforming method provided in this application includes the following steps:

[0082] Step S301: Based on the number of information streams used for information transmission, perform singular value decomposition on the obtained initial channel matrix to obtain the singular value decomposition result.

[0083] The singular value decomposition (SVD) results include a singular value matrix and a first singular vector. The dimension of the initial channel matrix is ​​determined based on the number of transmitting and receiving antennas used for information transmission. The initial channel matrix characterizes the information transmission coefficients of the channel from each transmitting antenna to each receiving antenna. The number of rows and columns in the singular value matrix is ​​equal to the number of information streams.

[0084] In one possible embodiment, if the number of transmitting antennas used by the base station to transmit information is N R The number of receiving antennas used for receiving information on the terminal device side is N. T Then the dimension of the initial channel matrix H is N. R ×N T .

[0085] The initial channel matrix can be subjected to singular value decomposition according to the following formula:

[0086] H=UΣV H

[0087] Where H represents the initial channel matrix; U represents the second singular vector; Σ represents the singular value matrix; V represents the first singular vector; V H Let represent the conjugate transpose of the first singular vector.

[0088] Singular value decomposition of the initial channel matrix is ​​performed according to the number of information streams used for information transmission. If the number of information streams is N... L .

[0089] The resulting U is of dimension N. R ×N LThe second singular vector, which may include multiple vectors.

[0090] The obtained Σ is of dimension N L ×N L The singular value matrix is ​​denoted by . The singular value matrix is ​​a diagonal matrix.

[0091] The obtained V H It is of dimension N L ×N T The conjugate transpose of the first singular vector, which may include multiple vectors.

[0092] It should be noted that the minimum of the number of transmitting antennas used to send information and the number of receiving antennas used to receive information is greater than or equal to the number of information streams.

[0093] For example, if the number of information streams N is taken L If Σ = 4, then Σ has a dimension of 4×4.

[0094] Step S302: Determine the power allocation coefficient matrix based on the set power allocation coefficients and singular value matrix.

[0095] In one possible embodiment, the singular value matrix is ​​calculated based on a set power allocation coefficient to determine the power allocation coefficient matrix.

[0096] The singular value matrix can be calculated using the following formula:

[0097] Φ=Σ -P

[0098] Where Φ represents the power allocation coefficient matrix and P represents the power allocation coefficient.

[0099] The power allocation factor is determined based on the differences in the information included in each information stream. The power allocation factor can be 0.5.

[0100] The power allocation coefficient matrix Φ is also N in dimension. L ×N L The matrix.

[0101] It should be noted that when selecting the power allocation coefficient, it should not be too large, otherwise it may lead to a significant decrease in the total gain of the information in each information stream.

[0102] Step S303: Based on the power allocation coefficient matrix and the first singular vector, shape the beam of the information stream used for transmitting information.

[0103] In one possible embodiment, power is allocated to the first singular vector according to the power allocation coefficient matrix to determine the allocation result matrix, a beamforming factor is determined based on the allocation result matrix, and the beamforming factor is used to shape the beam of the information stream used for transmitting information.

[0104] For example, the allocation result matrix is ​​first determined using the following formula:

[0105] V PA =VΦ

[0106] Among them, V PA Φ represents the allocation result matrix; Φ represents the power allocation coefficient matrix; V represents the first singular vector.

[0107] Through dimension N L ×N T The first singular vector multiplied by a dimension of N L ×N L The power allocation coefficient matrix Φ is obtained, resulting in a matrix of dimension N. L ×N L The allocation result matrix.

[0108] Where the dimension is N L ×N L Each column in the allocation result matrix represents a vector that assigns power to the information contained in the beam for each information stream.

[0109] After determining the allocation result matrix, the beamforming factor is determined based on the allocation result matrix. The specific process is as follows:

[0110] First, take the allocation result matrix V. PA The first two columns are subjected to a second-order Geometric Mean Decomposition (GMD) transformation to determine the first GMD transformation result. The specific GMD transformation process can be found in any method for performing GMD transformations on a matrix.

[0111] Then take the result of the second-order GMD transformation and the allocation result matrix V. PA The third column is subjected to a second-order GMD transformation to determine the result of the second GMD transformation.

[0112] Using the second GMD transformation result and the allocation result matrix V PA The fourth column is subjected to a second-order GMD transform. The second-order GMD transform is repeated in the above manner according to the amount of information flow until the final beamforming factor is obtained.

[0113] Taking an information stream of 4 as an example, the final beamforming factor is obtained. The beamforming factor is represented by W and expressed as W = V. PA G1=VΦG1.

[0114] In the above process, by allocating power among the information streams, the difference between the first singular vectors corresponding to the information in each information stream is further reduced.

[0115] In determining the beamforming factor, a geometric mean decomposition transformation is required. By reducing the difference between the first singular vectors corresponding to the information in each information stream, the information interference between the various information streams can be reduced during the geometric mean decomposition transformation.

[0116] After determining the beamforming factor, the beam used to transmit information is shaped.

[0117] By determining the beamforming factor using the above method, beamforming of the information stream used for information transmission can reduce the difference in the first singular vector values ​​of each information stream through inter-stream power allocation, thereby reducing inter-stream interference between information streams when beamforming the beam.

[0118] Furthermore, the inter-stream power allocation method used in the above method is based on the power of the first singular vector value in each information stream, which is equivalent to adaptively allocating inter-stream power according to the actual channel transmission scenario, and has a strong adaptability to the channel environment.

[0119] When all information streams are transmitted using a unified modulation and coding scheme (MCS), the overall transmission rate of information in the information stream within the channel can be guaranteed.

[0120] Figure 4 This paper illustrates a flowchart of another beamforming method provided in an embodiment of this application, as shown below. Figure 4 As shown in the embodiments of this application, another detailed beamforming method includes the following steps:

[0121] Step S401: Based on the number of information streams used for information transmission, perform singular value decomposition on the obtained initial channel matrix to obtain the singular value decomposition result.

[0122] Step S402: Determine the power allocation coefficient matrix based on the set power allocation coefficients and singular value matrix.

[0123] Step S403: Perform power allocation on the first singular vector according to the power allocation coefficient matrix to determine the allocation result matrix.

[0124] Step S404: Reorder each column in the allocation result matrix based on the rearrangement matrix to determine the reordered allocation result matrix corresponding to the allocation result matrix.

[0125] In one possible implementation, the columns in the allocation result matrix can be reordered. Specifically, the reordered allocation result matrix can be determined using the following formula:

[0126] V PA ′ =V PA ψ

[0127] Among them, V PA ′ V represents the reordering result matrix; ψ represents the reordering matrix; V PA This represents the allocation result matrix.

[0128] Let ψ represent the rearrangement matrix. The dimension of the rearrangement matrix is ​​determined by the amount of information flow, therefore, the dimension of the rearrangement matrix is ​​N. L ×N L .

[0129] For example, if N L =4, then the rearranged matrix is ​​a 4×4 matrix. Different sorting methods can be chosen according to different rearranged matrices. Choosing the sorting method [3, 2, 4, 1], the rearranged matrix can be:

[0130]

[0131] The rearrangement result matrix is ​​determined by multiplying the rearrangement matrix with the allocation result matrix.

[0132] The dimension of the above process is N. L ×N L The allocation result matrix is ​​reordered, which essentially changes the order in which the information in each information stream participates in power allocation. Depending on the actual situation, the inter-stream interference of the information streams that are ranked higher can be reduced by adjusting the order of participation in the calculation, thereby reducing the inter-stream interference of each information stream.

[0133] It should be noted that reordering the allocation result matrix also requires reordering the initial channel matrix. Furthermore, the initial channel matrix needs to be reordered using the same reordering matrix to achieve the goal of changing the inter-stream interference relationships without altering the information within each information stream.

[0134] Step S405: Perform geometric mean decomposition transformation on the reordering and allocation result matrix to determine the beamforming factor.

[0135] In one possible embodiment, after determining the reordering allocation result matrix, it is necessary to determine the beamforming factor based on the reordering allocation result matrix.

[0136] The beamforming factor can be determined by processing the allocation result matrix in step S203 above. The specific process will not be elaborated here; taking an information stream of 4 as an example, the final beamforming factor is obtained, and the beamforming factor determined by reordering the allocation result matrix is ​​represented by W. ′ The beamforming factor is expressed as W. ′ =V PA ′ G1 ′ =VΦψG1 ′ .

[0137] Step S406: Reorder the initial channel matrix based on the rearrangement matrix to determine the reordered channel matrix.

[0138] In one possible embodiment, the initial channel matrix is ​​reordered based on the reordered matrix obtained by reordering the allocation result matrix in step S304 above, and the reordered channel matrix is ​​determined.

[0139] Specifically, the reordered channel matrix can be determined using the following sorting method, as shown in the formula below:

[0140] H=UΣV H =U(ψψ H )Σ(ψψ H V H

[0141] Among them, ψψ H =ψ H ψ = I.

[0142] It should be noted that the rearranged matrix used to rearrange the allocation result matrix is ​​the same as the rearranged matrix used to rearrange the initial channel matrix. By using the same rearranged matrix, the information represented by the vectors in each column of the final calculated matrix remains unchanged.

[0143] The initial channel matrix is ​​reordered using the above method, resulting in a reordered channel matrix.

[0144] The reordered channel matrix is ​​as follows:

[0145] H ′ =U(ψψ H )Σ(ψψ H V H =(Uψ)(ψ H Σψ)(ψ H V H )

[0146] As can be seen from the above method, reordering does not change the value of the initial channel matrix, but only changes the order in which information in each information stream of the initial channel matrix participates in the operation.

[0147] The reordered channel matrix can be:

[0148]

[0149] By determining the order in which each column of the initial channel matrix participates in the computation, it is equivalent to changing the order in which information in each information stream participates in the computation, thereby altering the interference relationships between the information in each information stream.

[0150] Furthermore, during information transmission through the downlink channel, downlink receiver detection algorithms often assume that information in later-ordered streams has a lower signal-to-interference-and-noise ratio (SINR), thus applying stronger interference suppression to these weaker streams. Therefore, during beamforming, the order in which information from each stream participates in computation can be changed. This primarily alters the previous practice of calculating larger values ​​together; by changing the order of computation, larger and smaller values ​​can be combined, thereby reducing inter-stream interference between information streams overall.

[0151] When the receiving end has a detection algorithm that strongly suppresses inter-stream interference for later-ordered information streams, the inter-stream interference experienced by earlier-ordered information streams can be reduced, while the inter-stream interference for later-ordered information streams can be appropriately increased. During reception, the detection algorithm at the receiving end will also suppress the inter-stream interference experienced by later-ordered information streams. This ensures that the magnitude of inter-stream interference for each information stream matches the receiving end's interference suppression capability for each information stream.

[0152] Step S407: Beamform each column vector in the reordered channel matrix using beamforming factors.

[0153] In this context, each column vector in the reordering channel matrix represents the beam of an information stream.

[0154] In one possible embodiment, in the application scenario of downlink channel transmission, the final downlink transmission equivalent matrix can be obtained by beamforming each vector in the reordering matrix.

[0155] The specific beamforming process can be represented by the following formula:

[0156] H e=H ′ W ′

[0157] The above process completes the beamforming process for information in each information stream. The beamforming method provided in this application embodiment can be compared with other beamforming methods in the following ways.

[0158] 5G mobile communication systems employ multi-antenna technology. During downlink transmission, beamforming can be performed using multiple antennas. This involves adjusting the amplitude and phase of each transmitting antenna to create a specific beam direction, increasing signal strength in the desired direction and decreasing signal strength in areas of interference. This improves the signal-to-noise ratio (SNR) or signal-to-interference-plus-noise ratio (SINR) at the receiver, thus achieving array gain. This array gain is the beamforming amplitude gain matrix corresponding to the downlink transmission equivalent matrix.

[0159] The downlink transmission equivalent matrix determined in the above formula can be expanded to determine the beamforming amplitude gain matrix corresponding to the downlink transmission equivalent matrix.

[0160] Expanding the above formula, the calculation process is shown below:

[0161] H ′ W=((Uψ)(ψ H Σψ)(ψ H V H ))(VΦψG1 ′ )

[0162] =(Uψ)(ψ H Σψ)ψ H V H VΦψG1=(Uψ)(ψ H Σψ)Φ sort G1 ′

[0163] =U sort Σ sort Φ sort G1 ′

[0164] The final beamforming amplitude gain matrix can be obtained by transforming the above results. The transformation process is as follows:

[0165]

[0166] Among them, R sort This represents the beamforming amplitude gain matrix corresponding to the downlink transmission equivalent matrix.

[0167] Figure 5A schematic diagram comparing a beamforming amplitude gain matrix with the beamforming amplitude gain matrix provided in the embodiments of this application is shown.

[0168] like Figure 5 As shown, the numbers on the diagonal of the beamforming amplitude gain matrix determined by EBB (Eigenvalue Based Beamforming) represent the gain amplitude. It can be seen that the beamforming method using EBB cannot guarantee the same gain amplitude for information in each information stream, and therefore cannot guarantee the same overall transmission rate for each information stream. Therefore, the beamforming method using EBB is not suitable when using a unified modulation and coding strategy for transmission. However, through... Figure 5 It is obvious that the embodiments provided in this application are as follows: Figure 4 The beamforming method shown applies equal beamforming amplitude gain to each information stream. When using a multi-stream unified modulation and coding strategy for transmission, this ensures a consistent overall transmission rate.

[0169] Secondly Figure 6 A schematic diagram comparing another beamforming amplitude gain matrix with the beamforming amplitude gain matrix provided in the embodiments of this application is shown.

[0170] like Figure 6 As shown, when employing a multi-stream unified modulation and coding strategy, if the beamforming amplitude gain matrix obtained through beamforming using GMD represents the inter-stream interference between different information streams (excluding the diagonal lines), the information streams that are prioritized earlier experience greater inter-stream interference, and the overall inter-stream interference is relatively high, leading to significant performance differences between the signals of each stream. However, the embodiments provided in this application... Figure 4 The beamforming amplitude gain matrix obtained by the beamforming method shown exhibits significantly lower inter-stream interference for each information stream. Furthermore, in this embodiment, the inter-stream interference is lower for streams ranked earlier and higher for streams ranked later. This is achieved by reordering the information streams to adjust their computational order. In situations where the receiver's detection algorithm may have stronger interference suppression for later-ranked streams, the method provided in this embodiment ensures that the magnitude of inter-stream interference for each information stream matches the receiver's interference suppression capability for each stream.

[0171] Optionally, the above as follows Figure 4The beamforming method shown can first perform a reordering process for the initial channel matrix, and then perform a reordering process for the allocation result matrix. During the execution, it is only necessary to ensure that the reordered matrix for reordering the initial channel matrix is ​​the same as the reordered matrix for reordering each column of the allocation result matrix.

[0172] Based on the same concept, this application also provides an electronic device. Figure 7 An electronic device provided in an embodiment of this application is shown, the electronic device as follows: Figure 7 As shown, it includes:

[0173] The decomposition unit 701 is used to perform singular value decomposition on the acquired initial channel matrix based on the number of information streams used for transmitting information, and obtain the singular value decomposition result.

[0174] Allocation unit 702 is used to determine the power allocation coefficient matrix based on the set power allocation coefficients and singular value matrix;

[0175] Beamforming unit 703 is used to shape the beam of the information stream used for transmitting information based on the power allocation coefficient matrix and the first singular vector.

[0176] In one possible implementation, the decomposition unit 701 is further configured to:

[0177] The initial channel matrix is ​​decomposed using the following formula:

[0178] H=UΣV H

[0179] Where H represents the initial channel matrix; U represents the second singular vector; Σ represents the singular value matrix; V represents the first singular vector; V H Let represent the conjugate transpose of the first singular vector.

[0180] In one possible implementation, the beamforming unit 703 is further configured to:

[0181] Power allocation is performed on the first singular vector based on the power allocation coefficient matrix, and the allocation result matrix is ​​determined.

[0182] Based on the allocation result matrix, determine the beamforming factor;

[0183] Based on the beamforming factor, the beam used to transmit information is shaped.

[0184] In one possible implementation, the beamforming unit 703 is further configured to:

[0185] The allocation result matrix is ​​determined using the following formula:

[0186] VPA =VΦ

[0187] Among them, V PA Φ represents the allocation result matrix; Φ represents the power allocation coefficient matrix; V represents the first singular vector.

[0188] In one possible implementation, the beamforming unit 703 is further configured to:

[0189] The allocation result matrix is ​​reordered based on the rearrangement matrix to determine the reordered allocation result matrix corresponding to the allocation result matrix; each column in the allocation result matrix represents a vector for power allocation of the information contained in the beam of each information stream.

[0190] The reordering result matrix is ​​subjected to geometric mean decomposition transformation to determine the beamforming factor.

[0191] In one possible implementation, the beamforming unit 703 is further configured to:

[0192] The reordering result matrix corresponding to the allocation result matrix is ​​determined using the following formula:

[0193] V PA ′ =V PA ψ

[0194] Among them, V PA ′ V represents the reordering result matrix; ψ represents the reordering matrix; V PA This represents the allocation result matrix.

[0195] In one possible implementation, the beamforming unit 703 is further configured to:

[0196] The initial channel matrix is ​​rearranged based on the rearrangement matrix to determine the rearranged channel matrix;

[0197] Beamforming is performed on each column vector in the reordered channel matrix using beamforming factors; where each column vector in the reordered channel matrix represents the beam of an information stream.

[0198] Based on the same technical concept, this application also provides another electronic device that can realize the signing embodiment. Figure 8 The flow of the method being executed.

[0199] Figure 8 A schematic diagram of the structure of the electronic device provided in the embodiment of this application is shown, that is, a schematic diagram of the structure of the electronic device is shown. Figure 8 As shown, the electronic device includes a processor 801, a memory 802, and a transceiver 803;

[0200] Processor 801 is responsible for managing the bus architecture and general processing, while memory 802 stores data used by processor 801 during operation. Transceiver 803 is used to receive and send data under the control of processor 801.

[0201] The bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits together, represented by one or more processors (processor 801) and memory (memory 802). The bus architecture can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides the interface. Processor 801 is responsible for managing the bus architecture and general processing, and memory 802 can store data used by processor 801 during operation.

[0202] Processor 801 is responsible for managing the bus architecture and general processing, while memory 802 stores data used by processor 801 during operation. Transceiver 803 is used to receive and transmit information via an antenna array under the control of processor 801.

[0203] The bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits together, represented by one or more processors (processor 801) and memory (memory 802). The bus architecture can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides the interface. Processor 801 is responsible for managing the bus architecture and general processing, and memory 802 can store data used by processor 801 during operation.

[0204] The processor 801 can read the computer program in the memory and execute the following steps: based on the number of information streams used for transmitting information, perform singular value decomposition on the acquired initial channel matrix to obtain the singular value decomposition result; determine the power allocation coefficient matrix based on the set power allocation coefficients and the singular value matrix; and shape the beam of the information streams used for transmitting information based on the power allocation coefficient matrix and the first singular vector.

[0205] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, can be used to implement the beamforming method described in any embodiment of this application.

[0206] This application also provides a computer program product. Various aspects of the beamforming method provided in this application can also be implemented as a program product, including computer-executable instructions. These instructions are used to cause a computer to perform the steps of the beamforming method according to the various exemplary embodiments of this application described above. For example, an electronic device can perform... Figure 3 The flowchart of the beamforming method in steps S301 to S303 is shown.

[0207] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0208] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can 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, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0209] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0210] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0211] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A beamforming method, characterized in that, The method includes: Based on the number of information streams used for information transmission, singular value decomposition is performed on the acquired initial channel matrix to obtain the singular value decomposition result; the singular value decomposition result includes a singular value matrix, a first singular vector, and a second singular vector; the dimension of the initial channel matrix is ​​determined based on the number of transmitting antennas and the number of receiving antennas used for information transmission. Based on the set power allocation coefficients and the singular value matrix, the power allocation coefficient matrix is ​​determined; Power allocation is performed on the first singular vector according to the power allocation coefficient matrix to determine the allocation result matrix; The allocation result matrix is ​​reordered based on the rearrangement matrix to determine the reordered allocation result matrix corresponding to the allocation result matrix; each column of the allocation result matrix represents a vector for power allocation of the information contained in the beam of each information stream. Perform geometric mean decomposition transformation on the reordering and allocation result matrix to determine the beamforming factor; Based on the beamforming factor, the beam of the information stream used for transmitting information is shaped; The initial channel matrix is ​​decomposed using the following formula: H=UΣV H Wherein, H represents the initial channel matrix; U represents the second singular vector; Σ represents the singular value matrix; V represents the first singular vector; V H Let represent the conjugate transpose of the first singular vector.

2. The method according to claim 1, characterized in that, The initial channel matrix is ​​used to characterize the information transmission coefficients of the channel from each of the transmitting antennas to each of the receiving antennas; the number of rows and columns of the singular value matrix are both the number of information streams.

3. The method according to claim 1, characterized in that, The power allocation coefficient is a power allocation coefficient matrix; the step of allocating power to the first singular vector based on the power allocation coefficient matrix and determining the allocation result matrix includes: The allocation result matrix is ​​determined using the following formula: V PA <VΦ Among them, V PA Φ represents the allocation result matrix; Φ represents the power allocation coefficient matrix; V represents the first singular vector.

4. The method according to claim 1, characterized in that, The step of reordering each column of the allocation result matrix based on the rearrangement matrix to determine the reordered allocation result matrix corresponding to the allocation result matrix includes: The reordering allocation result matrix corresponding to the allocation result matrix is ​​determined using the following formula: V PA ′ =V PA ψ Among them, V PA ′ V represents the reordering and allocation result matrix; ψ represents the reordering matrix; V PA This represents the allocation result matrix.

5. The method according to claim 1 or 4, characterized in that, The number of rows and columns in the rearrangement matrix are both equal to the number of information streams.

6. The method according to claim 1, characterized in that, The step of shaping the beam of the information stream used for transmitting information based on the beamforming factor includes: The initial channel matrix is ​​reordered based on the rearrangement matrix to determine the reordered channel matrix; The beamforming factor is used to beamform each column vector in the reordered channel matrix; wherein each column vector in the reordered channel matrix represents a beam of the information stream.

7. An electronic device, characterized in that, The electronic device includes: The decomposition unit is used to perform singular value decomposition on the acquired initial channel matrix based on the number of information streams used for information transmission, to obtain the singular value decomposition result; the singular value decomposition result includes a singular value matrix, a first singular vector, and a second singular vector; the dimension of the initial channel matrix is ​​determined based on the number of transmitting antennas and the number of receiving antennas used for information transmission; the initial channel matrix is ​​decomposed using the following formula: H=UΣV H Wherein, H represents the initial channel matrix; U represents the second singular vector; Σ represents the singular value matrix; V represents the first singular vector; V H Let represent the conjugate transpose of the first singular vector; The allocation unit is used to determine the power allocation coefficient matrix based on the set power allocation coefficients and the singular value matrix; A beamforming unit is configured to allocate power to the first singular vector according to the power allocation coefficient matrix to determine an allocation result matrix; reorder each column of the allocation result matrix based on a rearrangement matrix to determine a reordered allocation result matrix corresponding to the allocation result matrix; each column of the allocation result matrix represents a vector for power allocation of information contained in the beam of each information stream; perform geometric mean decomposition transformation on the reordered allocation result matrix to determine a beamforming factor; and shape the beam of the information stream used for transmitting information based on the beamforming factor.

8. An electronic device, characterized in that, include: Memory, transceiver, and processor; The memory is used to store computer programs; The transceiver is used to send and receive information under the control of the processor; The processor is configured to read the computer program in the memory and execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

10. A computer program product, characterized in that, It includes computer-executable instructions for causing a computer to perform the method as described in any one of claims 1 to 6.

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