Beamforming method, network equipment, device and storage medium

Abstract

The embodiments of the present application provide a beamforming method, network device, apparatus, and storage medium, wherein the method includes: determining the shaping factors of multiple data streams of a user device based on a first matrix and a second matrix; the first matrix is ​​a channel estimation matrix for communication between the user device and the network device, and the second matrix is ​​determined based on a power iteration vector corresponding to each of the multiple data streams of the user device; and beamforming the user device according to the shaping factors of the multiple data streams of the user device. The present application determines the second matrix by a power iteration vector corresponding to each of the multiple data streams of the user device, performs multi-dimensional power iteration based on the second matrix and the channel estimation matrix for communication between the user device and the network device, implements eigenvalue decomposition in one loop, obtains eigenvectors of the multiple data streams of the user device in one loop, and simplifies the complexity of eigenvalue decomposition in the beamforming process.

Description

Technical Field

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

[0002] In the fifth generation mobile communication (5G), massive antennas generate high-gain beams directed toward user equipment (UE) through beamforming, enabling network equipment to transmit downlink data to user equipment (UE) with less downlink power, thereby improving energy efficiency in multiple-input multiple-output (MIMO) scenarios.

[0003] Eigenvalue decomposition is often used in beamforming algorithms. Eigenvalue decomposition is usually implemented by looping based on the power method. Each loop can extract the eigenvector with the largest eigenvalue from the correlation matrix. After multiple loops, the eigenvalue decomposition of the correlation matrix is ​​achieved, thereby mapping the eigenvector corresponding to the larger eigenvalue to the original channel space to obtain the shaping factor.

[0004] However, the computational complexity of the eigenvalue decomposition based on multiple loops is large, and errors accumulate in each loop, which results in a large number of iterations of the power method and makes it difficult to reduce the number of loops. Summary of the Invention

[0005] In response to the problems existing in the prior art, embodiments of the present application provide a beamforming method, network equipment, apparatus, and storage medium.

[0006] In a first aspect, an embodiment of the present application provides a beamforming method, including:

[0007] Determining shaping factors for multiple data streams of a user device based on a first matrix and a second matrix; the first matrix is ​​a channel estimation matrix for communication between the user device and a network device, and the second matrix is ​​determined based on a power iteration vector corresponding to each data stream in the multiple data streams of the user device;

[0008] Beamforming is performed on the user equipment according to the shaping factors of the multiple data streams of the user equipment.

[0009] Optionally, determining shaping factors of multiple data streams of the user equipment based on the first matrix and the second matrix includes:

[0010] updating the second matrix according to the first matrix;

[0011] Normalizing the updated second matrix;

[0012] The steps of updating the second matrix and normalizing the updated second matrix are iterated, and when a preset number of iterations is reached, shaping factors of the multiple data streams of the user equipment are determined.

[0013] Optionally, determining the shaping factors of the multiple data streams of the user equipment satisfies the following calculation formula:

[0014] w=Hu

[0015] Wherein, w represents the shaping factor of multiple data streams of the user equipment; H represents the first matrix; and u represents the second matrix obtained after the preset number of iterations.

[0016] Optionally, updating the second matrix according to the first matrix includes:

[0017] determining a correlation matrix of the first matrix;

[0018] The second matrix is ​​updated according to a correlation matrix of the first matrix.

[0019] Optionally, updating the second matrix according to the correlation matrix of the first matrix satisfies the following calculation formula:

[0020] u k+1 =Ru k

[0021] Among them, u k+1 represents the second matrix obtained after the k+1th iteration; R represents the correlation matrix of the first matrix; u k represents the second matrix obtained after the k-th iteration.

[0022] Optionally, the correlation matrix of the first matrix is ​​determined to satisfy the following calculation formula:

[0023]

[0024] Wherein, R represents the correlation matrix of the first matrix, H represents the first matrix, represents the conjugate transposed matrix of H.

[0025] Optionally, normalizing the updated second matrix includes:

[0026] Perform Schmidt orthogonalization on the updated second matrix.

[0027] Optionally, the performing beamforming on the user equipment according to the shaping factors of the multiple data streams of the user equipment includes:

[0028] Performing power normalization processing on shaping factors of multiple data streams of the user equipment;

[0029] Beamforming is performed on the user equipment according to the normalized shaping factors of the multiple data streams of the user equipment.

[0030] Optionally, the power normalization processing performed on the shaping factors of the multiple data streams of the user equipment satisfies the following calculation formula:

[0031]

[0032] Among them, w i The i-th column vector in the shaping factor w of the multiple data streams of the user equipment, i is any integer in the interval [1, J], J is the number of streams of the multiple data streams of the user equipment, w′ i represents the power normalized w i ,||w i || represents w i The modulus value of .

[0033] In a second aspect, an embodiment of the present application further provides a network device, including a memory, a transceiver, and a processor:

[0034] A memory for storing a computer program; a transceiver for transmitting and receiving data under the control of a processor; and a processor for reading the computer program in the memory and performing the following steps:

[0035] Determining shaping factors for multiple data streams of a user device based on a first matrix and a second matrix; the first matrix is ​​a channel estimation matrix for communication between the user device and a network device, and the second matrix is ​​determined based on a power iteration vector corresponding to each data stream in the multiple data streams of the user device;

[0036] Beamforming is performed on the user equipment according to the shaping factors of the multiple data streams of the user equipment.

[0037] Optionally, determining shaping factors of multiple data streams of the user equipment based on the first matrix and the second matrix includes:

[0038] updating the second matrix according to the first matrix;

[0039] Normalizing the updated second matrix;

[0040] The steps of updating the second matrix and normalizing the updated second matrix are iterated, and when a preset number of iterations is reached, shaping factors of the multiple data streams of the user equipment are determined.

[0041] Optionally, determining the shaping factors of the multiple data streams of the user equipment satisfies the following calculation formula:

[0042] w=Hu

[0043] Wherein, w represents the shaping factor of multiple data streams of the user equipment; H represents the first matrix; and u represents the second matrix obtained after the preset number of iterations.

[0044] Optionally, updating the second matrix according to the first matrix includes:

[0045] determining a correlation matrix of the first matrix;

[0046] The second matrix is ​​updated according to a correlation matrix of the first matrix.

[0047] Optionally, updating the second matrix according to the correlation matrix of the first matrix satisfies the following calculation formula:

[0048] u k+1 =Ru k

[0049] Among them, u k+1 represents the second matrix obtained after the k+1th iteration; R represents the correlation matrix of the first matrix; u k represents the second matrix obtained after the k-th iteration.

[0050] Optionally, the correlation matrix of the first matrix is ​​determined to satisfy the following calculation formula:

[0051]

[0052] Wherein, R represents the correlation matrix of the first matrix, H represents the first matrix, represents the conjugate transposed matrix of H.

[0053] Optionally, normalizing the updated second matrix includes:

[0054] Perform Schmidt orthogonalization on the updated second matrix.

[0055] Optionally, the performing beamforming on the user equipment according to the shaping factors of the multiple data streams of the user equipment includes:

[0056] Performing power normalization processing on shaping factors of multiple data streams of the user equipment;

[0057] Beamforming is performed on the user equipment according to the normalized shaping factors of the multiple data streams of the user equipment.

[0058] Optionally, the power normalization processing performed on the shaping factors of the multiple data streams of the user equipment satisfies the following calculation formula:

[0059]

[0060] Among them, w i The i-th column vector in the shaping factor w of the multiple data streams of the user equipment, i is any integer in the interval [1, J], J is the number of streams of the multiple data streams of the user equipment, w′ i represents the power normalized w i ,||w i || represents w i The modulus value of .

[0061] In a third aspect, an embodiment of the present application further provides a beamforming device, including:

[0062] A first determining unit is configured to determine a shaping factor of multiple data streams of a user equipment based on a first matrix and a second matrix; the first matrix is ​​a channel estimation matrix for communication between the user equipment and a network device, and the second matrix is ​​determined based on a power iteration vector corresponding to each data stream in the multiple data streams of the user equipment;

[0063] A beamforming unit is configured to perform beamforming on the user equipment according to the forming factors of the multiple data streams of the user equipment.

[0064] In a fourth aspect, an embodiment of the present application further provides a processor-readable storage medium, which stores a computer program, and the computer program is used to enable the processor to execute the steps of the beamforming method provided in the first aspect above.

[0065] In a fifth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is used to enable a computer to execute the steps of the beamforming method provided in the first aspect as described above.

[0066] In a sixth aspect, an embodiment of the present application further provides a communication device-readable storage medium, wherein the communication device-readable storage medium stores a computer program, and the computer program is used to enable the communication device to execute the steps of the beamforming method provided in the first aspect as described above.

[0067] In a seventh aspect, an embodiment of the present application further provides a chip product readable storage medium, wherein the chip product readable storage medium stores a computer program, and the computer program is used to enable the chip product to execute the steps of the beamforming method provided in the first aspect as described above.

[0068] The beamforming method, network device, apparatus, and storage medium provided in the embodiments of the present application determine a second matrix through a power iteration vector corresponding to each data stream of a user device, perform multidimensional power iteration based on the second matrix and a channel estimation matrix for communication between the user device and the network device, determine the shaping factors of the multiple data streams of the user device, and then perform beamforming based on the shaping factors of the multiple data streams of the user device, thereby implementing eigenvalue decomposition in one loop and obtaining eigenvectors of the multiple data streams of the user device in one loop, thereby simplifying the complexity of eigenvalue decomposition in the beamforming process. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the embodiments of the present application or the technical solutions in 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 application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0070] Figure 1 This is one of the flowcharts of the beamforming method provided in the embodiment of the present application;

[0071] Figure 2 This is the second flowchart of the beamforming method provided in the embodiment of the present application;

[0072] Figure 3 This is a schematic diagram of the structure of the network device provided in the embodiment of the present application;

[0073] Figure 4 Schematic diagram of the structure of the beamforming device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0074] In 5G mobile communications, massive antennas use beamforming to generate high-gain beams directed toward users. This allows network equipment to transmit downlink data to users with less downlink power, effectively improving energy efficiency in MIMO scenarios. Downlink beamforming includes two types: codebook and non-codebook. Non-codebook beamforming offers superior performance compared to codebook beamforming, as it maximizes the diversity gain in the channel space.

[0075] The non-codebook beamforming algorithm, based on uplink sounding channel estimation, goes through the process of sending uplink pilot signals, calculating beamforming factors, and sending downlink beams. Classic beamforming algorithms, such as the Eigenvalue Based Beamforming (EBB) algorithm, perform an orthogonal decomposition of the user channel space to obtain subchannels with maximized gain and orthogonal exchange, thereby obtaining the corresponding beamforming factors for the user equipment.

[0076] The traditional EBB beamforming algorithm can be implemented by performing singular value decomposition on the user device's channel estimate, or equivalently, by performing eigenvalue decomposition on the correlation matrix of the user channel estimate. The equivalent eigenvalue decomposition is a beamforming method with relatively low computational complexity when a lower-dimensional correlation matrix is ​​selected.

[0077] Traditional beamforming algorithms are usually implemented in multiple loops. The pseudo code for the outermost loop in one implementation process is as follows:

[0078]

[0079] Where i represents the number of flows, i.e. 1 flow per iteration; u i,0 represents the column vector corresponding to the i-th stream, where 0 represents the number of power iterations is 0, and the subscript k represents the number of power iterations; R represents the correlation matrix of the channel estimate to be eigenvalue decomposed; λ i,N represents the eigenvalue corresponding to the i-th stream, the subscript N corresponds to the number of the last iteration, and the superscript H represents the conjugate transpose.

[0080] In traditional beamforming algorithms, the number of cycles is the required number of streams. Each cycle can extract an eigenvector with the largest eigenvalue from the correlation matrix. After multiple cycles, the eigenvalue decomposition of the correlation matrix is ​​achieved, and then the eigenvector corresponding to the larger eigenvalue is mapped to the original channel space to obtain the beamforming factor.

[0081] However, this eigenvalue decomposition needs to be implemented in multiple loops, which requires a large amount of computation and causes error accumulation in each loop, resulting in a large number of iterations of the power method and making it difficult to reduce the number of loops.

[0082] In response to the above-mentioned problems existing in the prior art, the embodiments of the present application provide a beamforming method, network equipment, apparatus and storage medium, which simplifies the traditional single-stream power iteration algorithm of multiple cycles into an eigenvalue decomposition implemented in one cycle, and obtains the eigenvectors of multiple streams at the same time in one cycle, effectively simplifying the complexity of the eigenvalue decomposition of beamforming in MIMO scenarios, which is beneficial to the implementation of the shaping factor in limited hardware computing resources, thereby realizing beamforming.

[0083] In the embodiments of this application, the term "and / or" describes the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0084] In the embodiments of the present application, the term "plurality" refers to two or more than two, and other quantifiers are similar.

[0085] The technical solution provided in the embodiment of the present application can be applicable to a variety of systems, especially 5G systems. For example, applicable systems can be global system of mobile communication (Global System of Mobile Communication, GSM) system, code division multiple access (Code Division Multiple Access, CDMA) system, wideband code division multiple access (Wideband Code Division Multiple Access, WCDMA) general packet radio service (General Packet Radio Service, GPRS) system, long term evolution (Long Term Evolution, LTE) system, LTE frequency division duplex (FDD) system, LTE time division duplex (TDD) system, advanced long term evolution (Long Term Evolution Advanced, LTE-A) system, universal mobile system (Universal Mobile Telecommunication System, UMTS), global interconnection microwave access (Worldwide Interoperability for Microwave Access, WiMAX) system, 5G new air interface (New Radio, NR) system, etc. These multiple systems all include terminal equipment and network equipment. The system may also include core network parts, such as the Evolved Packet System (EPS), 5G System (5GS), etc.

[0086] The network device involved in the embodiments of the present application may be a base station, which may include multiple cells providing services to 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. The network device may be used to interchange received air frames with Internet Protocol (IP) packets, acting as a router between the wireless terminal device and the rest of the access network, wherein the rest of the access network may include an Internet Protocol (IP) communication network. The network device may also coordinate the attribute management of the air interface. For example, the network device involved in the embodiments of the present application may be a network device (Base Transceiver Station, BTS) in the Global System for Mobile Communications (GSM) or Code Division Multiple Access (CDMA), or a network device (NodeB) in Wide-band Code Division Multiple Access (WCDMA), or an evolved 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 femto, a pico, etc., which is not limited in the embodiments of the present application. In some network structures, the network device may include a centralized unit (CU) node and a distributed unit (DU) node, and the centralized unit and the distributed unit may also be geographically separated.

[0087] The terminal device involved in the embodiments of the present application may be a device that provides voice and / or data connectivity to a user, a handheld device with wireless connection function, or other processing equipment 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 can be a mobile terminal device, such as a mobile phone (or "cellular" phone) and a computer with a mobile terminal device. For example, it can be a portable, pocket-sized, handheld, computer-built-in or vehicle-mounted mobile device that exchanges language and / or data with a radio 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 (Subscriber Unit), a subscriber station (Subscriber Station), a mobile station (MobileStation), a mobile station (Mobile), a remote station (Remote Station), an access point (Access Point), a remote terminal device (Remote Terminal), an access terminal device (Access Terminal), a user terminal device (User Terminal), a user agent (User Agent), and a user device (User Device), but is not limited in the embodiments of the present application.

[0088] Network devices and terminal devices can each use one or more antennas for Multiple Input Multiple Output (MIMO) transmission. MIMO transmission can be either Single User MIMO (SU-MIMO) or Multi User MIMO (MU-MIMO). Depending on the configuration and number of antenna combinations, MIMO transmission can be 2D-MIMO, 3D-MIMO, FD-MIMO, or Massive-MIMO. It can also use diversity transmission, precoding, or beamforming.

[0089] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0090] The beamforming method provided in the embodiment of the present application can be applied to the beamforming process of the SU-MIMO system, and can also be applied to the beamforming process of a single user in the MU-MIMO system. The embodiment of the present application is described by applying it to the SU-MIMO system.

[0091] Figure 1 This is one of the flow charts of the beamforming method provided in the embodiment of the present application, such as Figure 1 As shown, the method can be applied to a network device, such as a base station, and the method includes at least the following steps:

[0092] Step 101: Determine shaping factors for multiple data streams of a user device based on a first matrix and a second matrix; the first matrix is ​​a channel estimation matrix for communication between the user device and a network device, and the second matrix is ​​determined based on a power iteration vector corresponding to each data stream in the multiple data streams of the user device.

[0093] Step 102: Perform beamforming on the user equipment according to the shaping factors of multiple data streams of the user equipment.

[0094] Specifically, the first matrix is ​​a channel estimation matrix for communication between the user equipment and the network equipment. The first matrix is ​​an M×N matrix, where M represents the number of base station antennas in the SU-MIMO system, and N represents the number of terminal antennas. Usually, the number of terminal antennas is much smaller than the number of base station antennas, and both M and N are positive integers.

[0095] The power iteration vector corresponding to each data stream of the user device is a column vector. An initial power iteration matrix, i.e., a second matrix, is constructed based on the power iteration vectors corresponding to each of the multiple data streams of the user device. Optionally, the matrix constructed from the power iteration vectors corresponding to each of the multiple data streams of the user device is normalized to further reduce the complexity of the subsequent eigenvalue decomposition process. The number of columns in the second matrix is ​​the number of data streams, i.e., the number of beamforming streams.

[0096] The second matrix may also be obtained by dividing the interference space matrix formed by the original channel estimation matrix of the user equipment into multiple low-rank matrices and performing normalization processing on the low-rank matrices.

[0097] The second matrix is ​​a multi-dimensional matrix and can be configured according to actual conditions. For example, the number of rows is configured to be N, and the number of columns is configured to be the number of data flows of the user equipment.

[0098] For example, for two streams, the number of columns of the second matrix may be 2; for three streams, the number of columns of the second matrix may be 3; for four streams, the number of columns of the second matrix may be 4.

[0099] This application simplifies the complexity of eigenvalue decomposition in the beamforming process for the case of multiple streams, so the number of beamforming streams is greater than or equal to 2, and the number of beamforming streams is less than or equal to the number of terminal antennas N.

[0100] Multi-dimensional power iteration is performed based on the first matrix and the second matrix to determine the shaping factors of multiple data streams of the user equipment, and then beamforming of the SU-MIMO system is completed according to the determined shaping factors.

[0101] The beamforming method provided in the embodiment of the present application determines a second matrix through a power iteration vector corresponding to each data stream of the multiple data streams of the user device, performs multi-dimensional power iteration based on the second matrix and a channel estimation matrix for communication between the user device and the network device, determines the shaping factors of the multiple data streams of the user device, and then performs beamforming based on the shaping factors of the multiple data streams of the user device, realizing eigenvalue decomposition in one loop, obtaining eigenvectors of the multiple data streams of the user device in one loop, and simplifying the complexity of eigenvalue decomposition in the beamforming process.

[0102] Optionally, determining shaping factors of multiple data streams of the user equipment based on the first matrix and the second matrix includes:

[0103] Update the second matrix according to the first matrix;

[0104] Normalizing the updated second matrix;

[0105] The steps of updating the second matrix and normalizing the updated second matrix are iterated, and when a preset number of iterations is reached, shaping factors of multiple data streams of the user equipment are determined.

[0106] Specifically, after determining the second matrix, the second matrix is ​​updated based on the first matrix, and then the updated second rectangle is normalized, and the steps of updating the second matrix and normalizing the updated second matrix are iterated.

[0107] Optionally, updating the second matrix according to the first matrix includes:

[0108] determining a correlation matrix for the first matrix;

[0109] The second matrix is ​​updated according to the correlation matrix of the first matrix.

[0110] Specifically, after determining the correlation matrix of the first matrix, the second matrix is ​​updated using the correlation matrix.

[0111] The correlation matrix of the first matrix is ​​determined by multiplying the first matrix and the conjugate transposed matrix of the first matrix. The obtained correlation matrix is ​​a self-conjugate matrix with a dimension of N×N, where N represents the number of terminal antennas.

[0112] Optionally, it is determined that the correlation matrix of the first matrix satisfies the following calculation formula:

[0113]

[0114] Where R represents the correlation matrix of the first matrix, H represents the first matrix, represents the conjugate transposed matrix of H.

[0115] The first matrix is ​​an M×N matrix, and its conjugate transposed matrix is ​​an N×M matrix, which ultimately results in an N×N correlation matrix. M represents the number of base station antennas, and N represents the number of terminal antennas. Generally speaking, the number of terminal antennas is much smaller than the number of base station antennas.

[0116] By using the correlation matrix of the first matrix to perform power iteration to update the second matrix, the eigenvalue decomposition in the user equipment beamforming process can be completed, and the eigenvector finally obtained is the beamforming factor.

[0117] Optionally, updating the second matrix according to the correlation matrix of the first matrix satisfies the following calculation formula:

[0118] u k+1 =Ru k

[0119] Among them, u k+1 represents the second matrix obtained after the k+1th iteration; R represents the correlation matrix of the first matrix; u k Represents the second matrix obtained after the kth iteration. A value of k of 0 indicates that the iteration has not yet begun.

[0120] Before the iteration begins, the second matrix can be normalized to further reduce the complexity of eigenvalue decomposition. Taking two streams and the number of terminal antennas N = 4 as an example, after normalizing the second matrix, the normalized second matrix u0 is obtained as follows:

[0121]

[0122] Taking the third stream as an example, after normalizing the second matrix, the normalized second matrix u0 is obtained as follows:

[0123]

[0124] Taking four streams as an example, after normalizing the second matrix, the normalized second matrix u0 is:

[0125]

[0126] After normalizing the second matrix, the correlation matrix is ​​used to update the second matrix. The normalized second matrix is ​​multiplied by the correlation matrix to update the second matrix.

[0127] In each iteration process, after the second matrix is ​​updated by using the correlation matrix, the updated second matrix is ​​normalized so that all power iteration matrices are normalized.

[0128] When the preset number of iterations is reached, shaping factors of multiple data streams of the user equipment are determined.

[0129] If the preset number of iterations is not reached, the steps of using the correlation matrix to update the second matrix and then normalizing the updated second matrix need to be repeated until the preset number of iterations is reached. Finally, the shaping factors of the multiple data streams of the user equipment are output.

[0130] Optionally, determining the shaping factors of the multiple data streams of the user equipment satisfies the following calculation formula:

[0131] w=Hu

[0132] Wherein, w represents the shaping factor of multiple data streams of the user equipment; H represents the first matrix; and u represents the second matrix obtained after a preset number of iterations.

[0133] Optionally, normalizing the updated second matrix includes:

[0134] Perform Schmidt orthogonalization on the updated second matrix.

[0135] Specifically, the updated second matrix can be normalized by Schmidt orthogonalization, and the Schmidt orthogonalization of the updated second matrix satisfies the following calculation formula:

[0136] u k+1 ′=Schmidt(u k+1 )

[0137] Among them, u k+1 represents the updated second matrix, u k+1 ′ represents the matrix obtained by performing Schmidt orthogonalization on the updated second matrix.

[0138] During the iteration process, the updated second matrix is ​​Schmidt orthogonalized so that the moduli of all power iteration matrices are normalized. There is no need to calculate the moduli of multiple power iteration column vectors after the iteration, which further simplifies the complexity.

[0139] Taking two streams as an example, the process of normalizing the updated second matrix can be expressed as:

[0140] u 1，k+1 =u 1，k+1 / ||u 1，k+1 ||

[0141]

[0142] u 2，k+1 =u 2，k+1 / ||u 2，k+1 ||

[0143] Among them, the left side of the equation represents the column vector after normalization, and the right side of the equation represents the process of normalizing the column vector. 1，k+1 Indicates u k+1 The first column of the column vector, u 2，k+1 Indicates u k+1 The column vector of the second column in .

[0144] First, the first column of the updated second matrix is ​​subjected to modular normalization, and then the second column is subjected to orthogonalization of the normalized first column, and finally the orthogonalized second column is subjected to modular normalization again.

[0145] Optionally, beamforming the user equipment according to the shaping factors of multiple data streams of the user equipment includes:

[0146] Performing power normalization on shaping factors of multiple data streams of a user equipment;

[0147] Beamforming is performed on the user equipment according to the normalized shaping factors of the multiple data streams of the user equipment.

[0148] Specifically, after the iteration ends and the shaping factors corresponding to the number of beamforming streams are output, power normalization is performed on the shaping factors of multiple data streams, and beamforming is performed using the shaping factors after power normalization, further reducing the complexity of beamforming.

[0149] Optionally, power normalization processing is performed on the shaping factors of the multiple data streams of the user equipment to satisfy the following calculation formula:

[0150]

[0151] Among them, w i The i-th column vector in the shaping factor w of the multiple data streams of the user equipment is represented, where i is any integer in the interval [1, J], J is the number of multiple data streams of the user equipment, and w′ i represents the power normalized w i ,||w i|| represents w i The modulus value of .

[0152] Taking two streams as an example, the shaping factor is normalized by power and can be expressed as:

[0153]

[0154] Wherein, w1 represents the first column vector in the shaping factor w of multiple data streams of the user equipment, w2 represents the second column vector in the shaping factor w of multiple data streams of the user equipment, w′1 represents w1 after power normalization, w′2 represents w2 after power normalization, and the double vertical lines represent modulo.

[0155] The beamforming method provided in the embodiment of the present application determines a second matrix through a power iteration vector corresponding to each data stream of the multiple data streams of the user device, iteratively updates the second matrix using a correlation matrix of the channel estimation matrix of the user device, determines the shaping factors of the multiple data streams of the user device, and then performs beamforming based on the shaping factors, realizing eigenvalue decomposition in one loop, and simultaneously obtaining eigenvectors of multiple data streams of the user device in one loop, thereby simplifying the complexity of eigenvalue decomposition in the beamforming process and facilitating the determination of shaping factors under limited hardware computing resources.

[0156] The technical solutions of the embodiments of the present application are further described below through several specific examples.

[0157] Example 1: Figure 2 This is the second flow chart of the beamforming method provided in the embodiment of the present application, such as Figure 2 As shown, in a SU-MIMO system, the number of base station antennas is M, the terminal has N = 4 antennas, the user's channel space H is an M × N matrix, and the number of user streams is 2 ≤ N. The method at least includes:

[0158] Step 201: Import the user's original channel estimation matrix and calculate the correlation matrix.

[0159] First calculate the correlation matrix:

[0160]

[0161] The correlation matrix R is a Hermitian matrix of dimension N×N, where N corresponds to the number of terminal antennas. Generally speaking, the number of terminal antennas is smaller than the number of base station antennas, and the user's channel space H is an M×N matrix.

[0162] Step 202: Randomly generate a 2-stream vector u and initialize power iteration.

[0163] Initialize the power iteration sequence number k = 0, and initialize the multi-dimensional power iteration vector u. For 2 streams, initialize the power iteration initial vector u0 as:

[0164]

[0165] Step 203: Update u using the power method.

[0166] Multiply the initialized power iteration vector by the correlation matrix and update the iteration vector, that is:

[0167] u k+1 =Ru k

[0168] Among them, u k+1 represents the power iteration vector obtained after the k+1th iteration; R represents the correlation matrix of the first matrix; u k Represents the power iteration vector obtained after the k-th iteration.

[0169] Step 204: Schmidt orthogonalization processing.

[0170] Perform Schmidt orthogonalization on a multidimensional iteration vector:

[0171] u k+1 ′=Schmidt(u k+1 )

[0172] For 2 streams, the orthogonalization process can be expressed as:

[0173] u 1，k+1 =u 1，k+1 / ||u 1，k+1 ||

[0174]

[0175] u 2，k+1 =u 2，k+1 / ||u 2，k+1 ||

[0176] The double vertical lines represent modulo.

[0177] Step 205: Determine whether the iteration end condition is met.

[0178] If the number of iterations has not been reached, the process returns to step 203 .

[0179] Step 206: When the iteration end condition is reached, the shaping factors of all data streams are output.

[0180] If the number of iterations is reached, the shape factors of all data streams are output:

[0181] w=Hu

[0182] Wherein, w represents the shaping factors of multiple data streams of the user equipment; H represents the original channel matrix of the user equipment; and u represents the power iteration vector obtained after a preset number of iterations.

[0183] Perform power normalization on the shaping factors of all data streams. For two streams, the specific results are as follows:

[0184]

[0185] Wherein, w1 represents the first column vector in the shaping factor w of multiple data streams of the user equipment, w2 represents the second column vector in the shaping factor w of multiple data streams of the user equipment, w′1 represents w1 after power normalization, w′2 represents w2 after power normalization, and the double vertical lines represent modulo.

[0186] The above steps 201 to 206 can be divided into three implementation modules:

[0187] Correlation Matrix Calculation Module: This module divides the interference space matrix formed by the original channel estimation space into multiple low-rank matrices to obtain the initial matrix for power iteration, which can be configured according to actual conditions. The correlation matrix is ​​determined based on the original channel estimation space matrix and its conjugate transposed matrix.

[0188] Multidimensional flow vector initialization module: The obtained power iteration initial matrix is ​​orthogonalized. Specifically, the orthogonal projection of a subspace onto another subspace is solved, and the corresponding subspace is updated with the orthogonal projection, that is, the orthogonalization of the two subspaces is completed, and the orthogonalization of all subspaces is gradually completed.

[0189] Multi-dimensional power iteration update flow vector module: reconstructs the projection matrix of the original channel estimation matrix using multiple orthogonalized subspaces. The order of the inversion matrix and the rank of the subspace involved are the same as the number of terminal antennas.

[0190] Example 2: Using the beamforming method provided in this application, taking iterative two-stream as an example, a pseudo code for implementing the loop in the process is as follows:

[0191]

[0192]

[0193] Figure 3 This is a schematic diagram of the structure of the network device provided in the embodiment of the present application. Figure 3 As shown, the network device includes a memory 301, a transceiver 302, and a processor 303, wherein:

[0194] The memory 301 is used to store computer programs; the transceiver 302 is used to send and receive data under the control of the processor 303.

[0195] Specifically, the transceiver 302 is configured to receive and send data under the control of the processor 303 .

[0196] Among them, Figure 3 In the embodiment, the bus architecture may include any number of interconnected buses and bridges, specifically linking together various circuits of one or more processors represented by processor 303 and memory represented by memory 301. The bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are all well known in the art and therefore will not be described further herein. The bus interface provides an interface. The transceiver 302 may be a plurality of components, i.e., a transmitter and a receiver, providing a unit for communicating with various other devices on a transmission medium, such as a wireless channel, a wired channel, an optical cable, and the like. The processor 303 is responsible for managing the bus architecture and general processing, and the memory 301 may store data used by the processor 303 when performing operations.

[0197] The processor 303 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or a complex programmable logic device (CPLD). The processor may also adopt a multi-core architecture.

[0198] The processor 303 is configured to read the computer program in the memory 301 and perform the following operations:

[0199] Determining shaping factors for multiple data streams of the user equipment based on a first matrix and a second matrix; the first matrix is ​​a channel estimation matrix for communication between the user equipment and the network device, and the second matrix is ​​determined based on a power iteration vector corresponding to each data stream in the multiple data streams of the user equipment;

[0200] Beamforming is performed on the user equipment according to the shaping factors of multiple data streams of the user equipment.

[0201] Optionally, determining shaping factors of multiple data streams of the user equipment based on the first matrix and the second matrix includes:

[0202] Update the second matrix according to the first matrix;

[0203] Normalizing the updated second matrix;

[0204] The steps of updating the second matrix and normalizing the updated second matrix are iterated, and when a preset number of iterations is reached, shaping factors of multiple data streams of the user equipment are determined.

[0205] Optionally, determining the shaping factors of the multiple data streams of the user equipment satisfies the following calculation formula:

[0206] w=Hu

[0207] Wherein, w represents the shaping factor of multiple data streams of the user equipment; H represents the first matrix; and u represents the second matrix obtained after a preset number of iterations.

[0208] Optionally, updating the second matrix according to the first matrix includes:

[0209] determining a correlation matrix for the first matrix;

[0210] The second matrix is ​​updated according to the correlation matrix of the first matrix.

[0211] Optionally, updating the second matrix according to the correlation matrix of the first matrix satisfies the following calculation formula:

[0212] u k+1 =Ru k

[0213] Among them, u k+1 represents the second matrix obtained after the k+1th iteration; R represents the correlation matrix of the first matrix; u k represents the second matrix obtained after the k-th iteration.

[0214] Optionally, it is determined that the correlation matrix of the first matrix satisfies the following calculation formula:

[0215]

[0216] Where R represents the correlation matrix of the first matrix, H represents the first matrix, represents the conjugate transposed matrix of H.

[0217] Optionally, normalizing the updated second matrix includes:

[0218] Perform Schmidt orthogonalization on the updated second matrix.

[0219] Optionally, beamforming the user equipment according to the shaping factors of multiple data streams of the user equipment includes:

[0220] Performing power normalization on shaping factors of multiple data streams of a user equipment;

[0221] Beamforming is performed on the user equipment according to the normalized shaping factors of the multiple data streams of the user equipment.

[0222] Optionally, power normalization processing is performed on the shaping factors of multiple data streams of the user equipment to satisfy the following calculation formula:

[0223]

[0224] Among them, w i The i-th column vector in the shaping factor w of the multiple data streams of the user equipment is represented, where i is any integer in the interval [1, J], J is the number of multiple data streams of the user equipment, and w′ i represents the power normalized w i ,||w i || represents w i The modulus value of .

[0225] It should be noted here that the above-mentioned network device provided in the embodiment of the present application can implement all the method steps implemented 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 those in the method embodiment will not be described in detail here.

[0226] Figure 4 is a structural diagram of a beamforming device provided in an embodiment of the present application, such as Figure 4 Shown, including:

[0227] A first determining unit 401 is configured to determine shaping factors for multiple data streams of a user equipment based on a first matrix and a second matrix; the first matrix is ​​a channel estimation matrix for communication between the user equipment and the network device, and the second matrix is ​​determined based on a power iteration vector corresponding to each data stream in the multiple data streams of the user equipment;

[0228] The beamforming unit 402 is configured to perform beamforming on the user equipment according to the shaping factors of the multiple data streams of the user equipment.

[0229] Optionally, the first determining unit is further configured to:

[0230] Update the second matrix according to the first matrix;

[0231] Normalizing the updated second matrix;

[0232] The steps of updating the second matrix and normalizing the updated second matrix are iterated, and when a preset number of iterations is reached, shaping factors of multiple data streams of the user equipment are determined.

[0233] Optionally, determining the shaping factors of the multiple data streams of the user equipment satisfies the following calculation formula:

[0234] w=Hu

[0235] Wherein, w represents the shaping factor of multiple data streams of the user equipment; H represents the first matrix; and u represents the second matrix obtained after a preset number of iterations.

[0236] Optionally, updating the second matrix according to the first matrix includes:

[0237] determining a correlation matrix for the first matrix;

[0238] The second matrix is ​​updated according to the correlation matrix of the first matrix.

[0239] Optionally, updating the second matrix according to the correlation matrix of the first matrix satisfies the following calculation formula:

[0240] u k+1 =Ru k

[0241] Among them, u k+1 represents the second matrix obtained after the k+1th iteration; R represents the correlation matrix of the first matrix; u k represents the second matrix obtained after the k-th iteration.

[0242] Optionally, it is determined that the correlation matrix of the first matrix satisfies the following calculation formula:

[0243]

[0244] Where R represents the correlation matrix of the first matrix, H represents the first matrix, represents the conjugate transposed matrix of H.

[0245] Optionally, normalizing the updated second matrix includes:

[0246] Perform Schmidt orthogonalization on the updated second matrix.

[0247] Optionally, the beamforming unit is further configured to:

[0248] Performing power normalization on shaping factors of multiple data streams of a user equipment;

[0249] Beamforming is performed on the user equipment according to the normalized shaping factors of the multiple data streams of the user equipment.

[0250] Optionally, power normalization processing is performed on the shaping factors of multiple data streams of the user equipment to satisfy the following calculation formula:

[0251]

[0252] Among them, w iThe i-th column vector in the shaping factor w of the multiple data streams of the user equipment is represented, where i is any integer in the interval [1, J], J is the number of multiple data streams of the user equipment, and w′ i represents the power normalized w i ,||w i || represents w i The modulus value of .

[0253] The methods and devices provided in the various embodiments of the present application are based on the same application concept. Since the methods and devices solve problems based on similar principles, the implementation of the devices and methods can refer to each other, and the repeated parts will not be repeated.

[0254] It should be noted that the division of units in the embodiments of the present application is schematic and is merely a logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0255] If the 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, 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 and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0256] It should be noted here that the above-mentioned device provided in the embodiment of the present application can implement all the method steps implemented 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 those in the method embodiment will not be described in detail here.

[0257] On the other hand, 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 configured to cause a processor to execute the beamforming method provided in each of the above embodiments, including:

[0258] Determining shaping factors for multiple data streams of the user equipment based on a first matrix and a second matrix; the first matrix is ​​a channel estimation matrix for communication between the user equipment and the network device, and the second matrix is ​​determined based on a power iteration vector corresponding to each data stream in the multiple data streams of the user equipment;

[0259] Beamforming is performed on the user equipment according to the shaping factors of multiple data streams of the user equipment.

[0260] The processor-readable storage medium can be any available medium or data storage device that can be accessed by the processor, including but not limited to magnetic storage (such as floppy disks, hard disks, tapes, magneto-optical disks (MO)), optical storage (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (such as ROMs, EPROMs, EEPROMs, non-volatile memories (NANDFLASH), solid-state drives (SSDs)), etc.

[0261] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can 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 can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage, etc.) that contain computer-usable program code.

[0262] 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 produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. 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.

[0263] 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 comprising an instruction device that implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0264] These processor-executable instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are performed 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 A step that specifies a function in one or more boxes.

[0265] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A beamforming method, characterized in that: include: determining shaping factors for multiple data streams of the user equipment based on the first matrix and the second matrix; The first matrix is ​​a channel estimation matrix for communication between the user equipment and the network device, and the second matrix is ​​determined based on a power iteration vector corresponding to each data stream in multiple data streams of the user equipment; performing beamforming on the user equipment according to the shaping factors of the multiple data streams of the user equipment; The determining of shaping factors of multiple data streams of the user equipment based on the first matrix and the second matrix includes: updating the second matrix according to the first matrix; Normalizing the updated second matrix; The steps of updating the second matrix and normalizing the updated second matrix are iterated, and when a preset number of iterations is reached, shaping factors of the multiple data streams of the user equipment are determined.

2. The beamforming method according to claim 1, wherein: The determining of the shaping factors of the multiple data streams of the user equipment satisfies the following calculation formula: w=Hu Wherein, w represents the shaping factor of multiple data streams of the user equipment; H represents the first matrix; and u represents the second matrix obtained after the preset number of iterations.

3. The beamforming method according to claim 1, wherein: The updating of the second matrix according to the first matrix includes: determining a correlation matrix of the first matrix; The second matrix is ​​updated according to a correlation matrix of the first matrix.

4. The beamforming method according to claim 3, wherein: The updating of the second matrix according to the correlation matrix of the first matrix satisfies the following calculation formula: in k+1 =Ru k Among them, u k+1 represents the second matrix obtained after the k+1th iteration; R represents the correlation matrix of the first matrix; u k represents the second matrix obtained after the k-th iteration.

5. The beamforming method according to claim 3, wherein: The correlation matrix of the first matrix is ​​determined to satisfy the following calculation formula: Wherein, R represents the correlation matrix of the first matrix, H represents the first matrix, represents the conjugate transposed matrix of H.

6. The beamforming method according to claim 1, wherein: The normalizing process on the updated second matrix includes: Perform Schmidt orthogonalization on the updated second matrix.

7. The beamforming method according to claim 1, wherein: The performing beamforming on the user equipment according to the shaping factors of the multiple data streams of the user equipment includes: Performing power normalization processing on shaping factors of multiple data streams of the user equipment; Beamforming is performed on the user equipment according to the normalized shaping factors of the multiple data streams of the user equipment.

8. The beamforming method according to claim 7, wherein: The power normalization processing performed on the shaping factors of the multiple data streams of the user equipment satisfies the following calculation formula: Among them, w i The i-th column vector in the shaping factor w of the multiple data streams of the user equipment is represented, where i is any integer in the interval [1, J], J is the number of the multiple data streams of the user equipment, and w′ i represents w after power normalization i ,||w i || represents w i The modulus value of .

9. A network device comprising a memory, a transceiver, and a processor; characterized in that: A memory for storing a computer program; a transceiver for transmitting and receiving data under the control of the processor; and a processor for reading the computer program in the memory and performing the following operations: Determining shaping factors for multiple data streams of a user device based on a first matrix and a second matrix; the first matrix is ​​a channel estimation matrix for communication between the user device and a network device, and the second matrix is ​​determined based on a power iteration vector corresponding to each data stream in the multiple data streams of the user device; performing beamforming on the user equipment according to the shaping factors of the multiple data streams of the user equipment; The determining of shaping factors of multiple data streams of the user equipment based on the first matrix and the second matrix includes: updating the second matrix according to the first matrix; Normalizing the updated second matrix; The steps of updating the second matrix and normalizing the updated second matrix are iterated, and when a preset number of iterations is reached, shaping factors of the multiple data streams of the user equipment are determined.

10. The network device according to claim 9, characterized in that The determining of the shaping factors of the multiple data streams of the user equipment satisfies the following calculation formula: w=Hu Wherein, w represents the shaping factor of multiple data streams of the user equipment; H represents the first matrix; and u represents the second matrix obtained after the preset number of iterations.

11. The network device according to claim 9, wherein: The updating of the second matrix according to the first matrix includes: determining a correlation matrix of the first matrix; The second matrix is ​​updated according to a correlation matrix of the first matrix.

12. The network device according to claim 11, wherein: The updating of the second matrix according to the correlation matrix of the first matrix satisfies the following calculation formula: in k+1 =Ru k Among them, u k+1 represents the second matrix obtained after the k+1th iteration; R represents the correlation matrix of the first matrix; u k represents the second matrix obtained after the k-th iteration.

13. The network device according to claim 11, wherein: The correlation matrix of the first matrix is ​​determined to satisfy the following calculation formula: Wherein, R represents the correlation matrix of the first matrix, H represents the first matrix, represents the conjugate transposed matrix of H.

14. The network device according to claim 9, wherein: The normalizing process on the updated second matrix includes: Perform Schmidt orthogonalization on the updated second matrix.

15. The network device according to claim 9, wherein: The performing beamforming on the user equipment according to the shaping factors of the multiple data streams of the user equipment includes: Performing power normalization processing on shaping factors of multiple data streams of the user equipment; Beamforming is performed on the user equipment according to the normalized shaping factors of the multiple data streams of the user equipment.

16. The network device according to claim 15, characterized in that The power normalization processing performed on the shaping factors of the multiple data streams of the user equipment satisfies the following calculation formula: Among them, w i The i-th column vector in the shaping factor w of the multiple data streams of the user equipment is represented, where i is any integer in the interval [1, J], J is the number of the multiple data streams of the user equipment, and w′ i represents w after power normalization i ,||w i || represents w i The modulus value of .

17. A beamforming device, characterized in that: include: A first determining unit, configured to determine shaping factors of multiple data streams of the user equipment based on the first matrix and the second matrix; The first matrix is ​​a channel estimation matrix for communication between the user equipment and the network device, and the second matrix is ​​determined based on a power iteration vector corresponding to each data stream in multiple data streams of the user equipment; a beamforming unit, configured to perform beamforming on the user equipment according to the forming factors of the multiple data streams of the user equipment; The determining of shaping factors of multiple data streams of the user equipment based on the first matrix and the second matrix includes: updating the second matrix according to the first matrix; Normalizing the updated second matrix; The steps of updating the second matrix and normalizing the updated second matrix are iterated, and when a preset number of iterations is reached, shaping factors of the multiple data streams of the user equipment are determined.

18. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is configured to cause a computer to execute the beamforming method according to any one of claims 1 to 8.

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