Precoding transmission method, system and equipment of cellular-free multi-antenna system

Abstract

The invention relates to a precoding transmission method, system and equipment of a cellular-free multi-antenna system. The method comprises the following steps: acquiring channel airspace covariance information of the system and noise power of a channel; calculating a sending characteristic matrix set of the system according to the channel airspace covariance information, and performing iterative optimization according to the channel airspace covariance information and noise power to obtain an optimal power allocation vector set of the system; calculating a precoding matrix of each access point based on the sending feature matrix set and the optimal power distribution vector set; and each access point transmits data in combination with the user data and the obtained precoding matrix. The system and the equipment are used for executing the method. According to the invention, the measurement and feedback overhead of the system can be obviously reduced, and the maximum of the average transmission rate of the system information can be realized under the condition that the sending power of each access point at the sending end is limited.

Description

Technical Field

[0001] The present invention relates to the technical field of digital communications, and in particular to a precoding transmission method, system and device for a non-cellular multi-antenna system. Background Art

[0002] Since the base station deployment of 5G communication system is more dense than that of 3G and 4G systems, the user interference between cells is serious, which seriously restricts the capacity of the system. In recent years, a decellularized multi-input multi-output (MIMO) communication network architecture has been proposed by researchers to solve the interference problem between users (especially edge users) in cells. Different from the traditional network architecture, this architecture is user-centric and has no obvious cell boundaries. The system deploys a large number of small access points (APs) and collaborates between access points to reduce or eliminate interference between users.

[0003] The design of the transmitter of the decellularized MIMO system is very important for improving system performance, and this aspect has received extensive attention from researchers. For the forward link of the decellularized massive MIMO network, the American IEEE Wireless Communications Letters (“Modified conjugate beamforming for cell-free massive MIMO”, IEEE Wireless Communications Letters, 2019, 8(2): 616–619) proposed an improved conjugate beamforming scheme. This scheme eliminates user self-interference. Although it does not use forward pilot signals, its performance is close to that achieved by using pilot signals. For the downlink of the decellularized millimeter-wave massive multi-antenna system, the American IEEE Communications Letters (“Max-min optimal beamforming for cell-free massive MIMO”, IEEE Communications Letters, 2020, 24(10): 2344–2348) studied the energy efficiency of the system and modeled the problem as a non-concave energy efficiency optimization problem. This paper decomposes the problem into two sub-problems: hybrid precoding design and power allocation, and proposes an algorithm to solve it based on theories such as fractional programming and non-cooperative games.

[0004] The design of the transmitter usually requires channel state information (CSI). CSI can be roughly divided into two categories: real-time CSI and statistical CSI. At present, there are many studies on the use of real-time CSI for transmitter design, while there are fewer related designs using statistical CSI, and there is still a lot of room for research. If the channel is fast fading, the frequent measurement and feedback of channel information by real-time CSI will inevitably bring a great burden to the system feedback link. Therefore, how to obtain channel state information while reducing the system burden to complete data transmission is a technical problem that needs to be solved urgently by technicians in this field. Summary of the invention

[0005] In order to solve the problems existing in the above-mentioned prior art, the present invention aims to provide a precoding transmission method, system and device for a non-cellular multi-antenna system. The present invention can significantly reduce the system measurement and feedback overhead, and maximize the average transmission rate of system information under the condition that the transmission power of each access point at the transmitting end is limited.

[0006] A precoding transmission method for a non-cellular multi-antenna system according to the present invention comprises the following steps:

[0007] S01. Obtain channel spatial covariance information and channel noise power of the system;

[0008] S02. Calculate a set of transmission characteristic matrices of the system according to the channel spatial covariance information, and iteratively optimize and obtain an optimal power allocation vector set of the system according to the channel spatial covariance information and noise power;

[0009] S03. Calculate a precoding matrix for each access point based on the transmission characteristic matrix set and the optimal power allocation vector set;

[0010] S04. Each access point combines the user data and the obtained precoding matrix to transmit data.

[0011] Preferably, in step S02, the sending feature matrix set of the computing system is specifically:

[0012] The system includes M access points and one user terminal. The access point number i = 1 ~ M. Access point i is equipped with N T,i Root transmitting antennas, the user end is equipped with N R The channel spatial covariance information of the system is recorded as The noise power of the channel is Among them, Θ R,i represents the receiving spatial covariance matrix of the access point i to the user end channel, Θ T,i represents the transmission spatial domain covariance matrix of the access point i to the user end channel;

[0013] According to the channel spatial covariance information perform eigenvalue decomposition on the transmit spatial covariance matrix Θ T,i :

[0014]

[0015] wherein represents the transmit eigenmatrix of the i-th access point in the system, represents the conjugate transpose operation of

[0016] Preferably, in step S02, iteratively optimizing to obtain the optimal power allocation vector set of the system according to the channel spatial covariance information and the noise power specifically includes the following steps:

[0017] S0201. Initialize the iteration index n = 0, the components of the auxiliary parameter μ and the iteration step size t > 0, wherein [μ1 … μ M T represents the matrix transpose of the set [μ1 … μ M . Calculate the initial value of the power allocation vector set according to the power allocation algorithm, and the power allocation algorithm includes the following steps:

[0018] a01. Initialize the loop index k = 0 and the search step size 0 < S < 1, initialize the set B = {b ij , i = 1~M, j = 1~4}, denoted as and satisfy the constraint wherein represents the maximum transmit power of access point i;

[0019] a02. Calculate the search direction set at the current iteration

[0020]

[0021] wherein:

[0022]

[0023] [M] ll represents taking the (l, l) element in the matrix M, l = 4(i - 1) + j;

[0024] is a 4M-dimensional column vector, defined as represents the matrix transpose of the set ;

[0025] ​ blkdg(.) represents the block diagonalization operation of the matrix, E(.) represents the expectation of the channel matrix H, and diag(.) represents the diagonalization operation. Represents the matrix The conjugate transpose operation of ;

[0026] H=[H1 H2 ... H M ], H i represents the channel matrix from access point i to the user end,

[0027] in,

[0028] i=1~M,

[0029]

[0030] g i represents the large-scale fading factor, H ω,i Indicates a dimension of N R ×N T,i A complex Gaussian random matrix, each element is independently counted and obeys a complex Gaussian distribution with a mean of 0 and a variance of 1;

[0031] a03. Based on the obtained search direction set Update the set B:

[0032]

[0033] a04, if satisfied is the preset threshold, then the optimal solution of the set B is obtained, which is recorded as If not satisfied, increase the loop index k and return to step a02;

[0034] a05. Calculate the power allocation vector set corresponding to the optimal solution of the set B As the initial value of the power allocation vector set:

[0035]

[0036] S202, updating the auxiliary parameter μ:

[0037]

[0038] in, represents the optimal solution of set B calculated by calling the power allocation algorithm for the nth time, P i Max Represents the power allocation vector set The maximum value of the elements in ;

[0039] S203, according to the updated auxiliary parameters The power allocation algorithm is used to calculate and obtain an updated power allocation vector set as an iterative value of the power allocation vector set;

[0040] S204: Based on the initial value and iterative value of the obtained power allocation vector set, determine whether convergence occurs. If convergence occurs, output the iterative value of the power allocation vector set as the optimal power allocation vector set, denoted as And end the loop; if not converged, update the iteration index n and return to step S202.

[0041] Preferably, the step S03 is specifically:

[0042] Calculate the precoding matrix W of each access point i :

[0043]

[0044] Based on the precoding matrix W of each access point i Construct a precoding matrix set {W i}.

[0045] Preferably, step S04 includes the following steps:

[0046] Each access point multiplies the user data by its own precoding matrix W i , synchronously transmit data.

[0047] A precoding transmission system of the present invention is used to execute the above method, including:

[0048] A measurement module, which is used to obtain the channel spatial covariance information and channel noise power of the system;

[0049] An iterative optimization module, which is used to calculate a set of transmission characteristic matrices of the system according to the channel spatial covariance information, and iteratively optimize and obtain an optimal power allocation vector set of the system according to the channel spatial covariance information and noise power;

[0050] A precoding matrix calculation module, which is used to calculate the precoding matrix of each access point based on the transmission characteristic matrix set and the optimal power allocation vector set;

[0051] The transmission module is used to control each access point to transmit data by combining user data and the obtained precoding matrix.

[0052] A computer device of the present invention comprises a processor and a memory connected by signals, wherein the memory stores at least one instruction or at least one program, and when the at least one instruction or the at least one program is loaded by the processor, the precoding transmission method as described above is executed.

[0053] A computer-readable storage medium of the present invention stores at least one instruction or at least one program, and when the at least one instruction or the at least one program is loaded by a processor, the precoding transmission method as described above is executed.

[0054] The precoding transmission method, system and device of a non-cellular multi-antenna system described in the present invention have the advantages that the present invention provides a precoding method for a non-cellular MIMO transmission system based on channel covariance and system signal-to-noise ratio feedback. The method can effectively improve the system information transmission rate while meeting the transmission power constraint of the system access point. Compared with some existing transmission schemes, such as isotropic transmission and beamforming methods, the information transmission rate achieved by this method is higher. The present invention also has the advantages of small feedback amount and low system overhead. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a flowchart of the steps of a precoding transmission method for a non-cellular multi-antenna system described in this embodiment;

[0056] Figure 2 is a flowchart of the steps of the power allocation algorithm described in this embodiment;

[0057] Figure 3 is a performance comparison chart of the method described in this embodiment and other methods;

[0058] Figure 4 It is a schematic diagram of the structure of the computer device described in this embodiment.

[0059] Description of reference numerals: 101 - processor, 102 - memory. DETAILED DESCRIPTION

[0060] like Figure 1 As shown, the precoding transmission method of a non-cellular multi-antenna system described in the present invention is exemplarily applied to a single-user decellularized MIMO system, the system includes M access points (Accesspoint, AP) and a user terminal, the access point sequence number i = 1 ~ M, access point i is equipped with N T,i Root transmitting antennas, the user end is equipped with N R The channel spatial covariance information of the system is recorded as The noise power of the channel is Among them, Θ R,irepresents the receiving spatial covariance matrix of the access point i to the user end channel, Θ T,i represents the transmission spatial covariance matrix of the access point i to the user end channel.

[0061] The access points are distributed, and each access point is connected to a central processing unit (CPU). All access points jointly provide data transmission services for users. The data received by users can be expressed as:

[0062]

[0063] in, represents the signal sent by the i-th access point, and the Gaussian white noise w obeys distributed, represents the channel matrix between the i-th access point and the user end. Under the control of the central processor, the access points transmit data synchronously. In order to reduce the data overhead of the link, the access points do not share the transmitted data. The transmission covariance matrix is ​​defined as At this time, the overall transmission covariance matrix Q of the system can be expressed as:

[0064] Q=blkdg({Q i}),

[0065] in, blkdg(·) represents the block diagonalization operation of the matrix, and the traversal capacity of the system is expressed as:

[0066]

[0067] Then the above channel matrix H i It can be further expressed as:

[0068]

[0069] Among them, g i represents the large-scale channel fading factor, Θ R,i represents the receiving spatial covariance matrix of the access point i to the user end channel, Θ T,i H represents the spatial covariance matrix of the channel from access point i to the user end. ω,i is a random matrix, each element of which is an independently distributed random variable, obeying a complex Gaussian distribution with mean 0 and variance 1.

[0070] For the convenience of performance comparison, the system signal-to-noise ratio is defined as follows:

[0071]

[0072] Among them, P i MaxIndicates the maximum transmit power of access point i.

[0073] Assume that the transmitter has channel statistical information: the channel spatial covariance information is The noise power of the channel is The goal of this embodiment is to design an optimal transmission covariance matrix to maximize the ergodic capacity of the system.

[0074] The operation of the transmitter can be divided into two stages: the training stage and the data transmission stage. In the training stage, each access point transmits a pilot signal, and the user receiver measures its own channel matrix, calculates the transmit-receive covariance matrix, and feeds it back to each access point. In addition, the receiver also calculates and feeds back the signal-to-noise ratio of the channel. The access point calculates the optimal precoding matrix set {W based on the received channel transmit-receive covariance matrix and the channel signal-to-noise ratio. i Then, the system enters the data transmission phase: the access point multiplies the user data by their respective precoding matrix W i , after CPU coordination and synchronization, it is sent out.

[0075] In this embodiment, the decellularized MIMO system consists of a single user terminal and M access points, where the number of transmitting and receiving antennas is 4. The maximum transmission power of all access points is the same: P i Max =P1 Max ,i=1~M。

[0076] The spatial correlation matrix is ​​exponential, with a basis of ρ, and the element value at the (m,n) position of the matrix is ​​denoted by ρ |m-n| .

[0077] Simulation settings ρ T,i =i / 10,ρ R,i =0.25, where ρ T,i and ρ R,i They are the sending spatial covariance matrix Θ T,i and the receiving spatial covariance matrix Θ R,i The base of.

[0078] In addition, setting the large-scale fading factor The noise power is set to Signal-to-noise ratio SNR=5dB.

[0079] The method described in this embodiment includes the following steps:

[0080] S01. Obtain the channel spatial covariance information and channel noise power of the system; specifically, the system obtains the channel noise power through a period of measurement. and channel spatial covariance information

[0081] S02. Calculate the set of transmission eigen - matrix of the system according to the channel spatial covariance information, and iteratively optimize to obtain the set of optimal power allocation vectors of the system according to the channel spatial covariance information and the noise power;

[0082] Specifically, calculating the set of transmission eigen - matrix of the system is as follows:

[0083] The system includes M access points and one user terminal. The access point serial number \(i = 1\sim M\). The access point \(i\) is equipped with N T,i transmitting antennas, and the user terminal is equipped with N R transmitting antennas. Denote the channel spatial covariance information of the system as The noise power of the channel is where \(\Theta\) R,i represents the receiving spatial covariance matrix of the channel from the access point \(i\) to the user terminal, and \(\Theta\) T,i represents the transmitting spatial covariance matrix of the channel from the access point \(i\) to the user terminal;

[0084] According to the channel spatial covariance information Perform eigenvalue decomposition on the transmitting spatial covariance matrix \(\Theta\) T,i :

[0085]

[0086] where represents the transmission eigen - matrix of the \(i\) - th access point in the system, represents the conjugate transpose operation of

[0087] In this step, iteratively optimizing to obtain the set of optimal power allocation vectors of the system according to the channel spatial covariance information and the noise power specifically includes the following steps:

[0088] S0201. Initialize the iteration index \(n = 0\), the component of the auxiliary parameter \(\mu\) and the iteration step size \(t>0\), where [\(\mu_1\cdots\mu\) M T represents the matrix transpose of the set \([\mu_1\cdots\mu\) M . Calculate the initial value of the power allocation vector set according to the power allocation algorithm, as shown in Figure 2 . The power allocation algorithm includes the following steps:

[0089] a01. Initialize the loop index \(k = 0\) and the search step size \(0\lt S\lt1\). Initialize the set \(B=\{b\) ij , \(i = 1\sim M,j = 1\sim4\}\), denoted as and satisfy the constraint where​ represents the maximum transmit power of access point i;

[0090] a02. Calculate the search direction set for the current iteration

[0091]

[0092] in:

[0093]

[0094] [M] ll It means taking the (l,l) element in the matrix M, l = 4(i-1) + j;

[0095] is a 4M-dimensional column vector, defined as Represents a set The matrix transpose of ;

[0096] blkdg(.) represents the block diagonalization operation of the matrix, E(.) represents the expectation of the channel matrix H, and diag(.) represents the diagonalization operation. Represents the matrix The conjugate transpose operation of ;

[0097] H=[H1 H2 … H M ], H i represents the channel matrix from access point i to the user end,

[0098] in,

[0099] i=1~M,

[0100]

[0101] g i represents the large-scale fading factor, H ω,i Indicates a dimension of N R ×N T,i A complex Gaussian random matrix, each element is independently counted and obeys a complex Gaussian distribution with a mean of 0 and a variance of 1;

[0102] a03. Based on the obtained search direction set Update the set B:

[0103]

[0104] a04, if satisfied is the preset threshold, then the optimal solution of the set B is obtained, which is recorded as If not satisfied, set the loop index k+1 and return to step a02;

[0105] a05. Calculate the power allocation vector set corresponding to the optimal solution of the set B As the initial value of the power allocation vector set:

[0106]

[0107] S202, updating the auxiliary parameter μ:

[0108]

[0109] in, represents the optimal solution of set B calculated by calling the power allocation algorithm for the nth time, P i Max Represents the power allocation vector set The maximum value of the elements in ;

[0110] S203, according to the updated auxiliary parameters The power allocation algorithm is used to calculate and obtain an updated power allocation vector set as an iterative value of the power allocation vector set;

[0111] S204: Based on the initial value and iteration value of the obtained power allocation vector set, determine whether convergence has occurred. For example, the initial value is The iteration value is Based on the initial value and iterator value Determine whether it is satisfied ε is a preset threshold. If it is satisfied, it is judged as convergence, and the iterative value of the power allocation vector set is output as the optimal power allocation vector set, which is recorded as If not, it is judged as non-convergence, the iteration index n is updated, and the process returns to step S202.

[0112] S03. Calculate the precoding matrix of each access point based on the transmission characteristic matrix set and the optimal power allocation vector set, specifically:

[0113] Calculate the precoding matrix W of each access point i :

[0114]

[0115] Based on the precoding matrix W of each access point i Construct a precoding matrix set {W i}.

[0116] S04. Each access point transmits data in combination with the user data and the obtained precoding matrix, specifically:

[0117] Each access point multiplies the user data by its own precoding matrix W i , synchronously transmit data.

[0118] Based on the obtained precoding matrix set {W i}, calculate the transmit covariance matrix Q of each access point i :

[0119]

[0120] According to the transmission covariance matrix Q of each access point i , Q i Substituting the above-mentioned transmit covariance matrix Q into the calculation formula, the transmit covariance matrix Q of the entire system can be obtained. Substituting the obtained transmit covariance matrix Q of the entire system into the above-mentioned system ergodic capacity C(Q) calculation formula, the system ergodic capacity C(Q) can be obtained.

[0121] Calculation instructions:

[0122] 1) Given the number of access points M, the maximum transmit power of the access point P i Max It can be obtained by reverse calculation from the system signal-to-noise ratio calculation formula based on the preset SNR=5dB;

[0123] 2) When calculating the expression with the expected operation E(.), firstly, Matlab generates several channel samples, and then substitutes them into the expression to find the average, instead of finding the expected operation. For example: in step a02, find the 4M×4M matrix M (at this time it is known that and the number of access points M). According to the channel matrix H i Expression, generates a set of 1000 channel samples: Then, the combination is based on the expression of the user receiving the receipt, and 1000 channel matrices are combined: H (m) , m = 1 to 1000. Finally, substitute the expression of matrix M, calculate 1000 M matrices, and average them to get the final result.

[0124] Figure 3 This is a performance comparison chart of the method of this embodiment and other methods in a 4-transmit 4-receive non-cellular MIMO system. This chart uses two benchmark algorithms for comparison.

[0125] 1) Equal power transmission method: All access points send data isotropically at maximum power, i.e.

[0126] 2) Beamforming method: Each access point uses a single beamforming method to inject all power into the "strongest" direction in space.

[0127] It can be seen that as the number of access points M increases, the average capacity also increases accordingly, because the increase in M ​​brings about spatial diversity gain. When M is small (M=1 or 2), the performance of the equal power transmission method is close to that of the method of the present invention; however, as M further increases, the average capacity cannot continue to increase and can only reach an asymptotic level - about 7.6 bits / symbol. The beamforming method performs poorly when M is small, but its performance increases rapidly as M increases. Comprehensively compared, the performance of the method of the present invention is significantly better than the other two existing methods.

[0128] The present invention provides a precoding method for a non-cellular MIMO transmission system based on channel covariance and system signal-to-noise ratio feedback. The method can effectively improve the system information transmission rate while meeting the transmission power constraint of the system access point. Compared with some existing transmission schemes, such as isotropic transmission and beamforming methods, the information transmission rate achieved by the method is higher. The present invention also has the advantages of small feedback amount and low system overhead.

[0129] This embodiment further provides a precoding transmission system, which is used to execute the above method, including:

[0130] A measurement module, which is used to obtain the channel spatial covariance information and channel noise power of the system;

[0131] An iterative optimization module, which is used to calculate a set of transmission characteristic matrices of the system according to the channel spatial covariance information, and iteratively optimize and obtain an optimal power allocation vector set of the system according to the channel spatial covariance information and noise power;

[0132] A precoding matrix calculation module, which is used to calculate the precoding matrix of each access point based on the transmission characteristic matrix set and the optimal power allocation vector set;

[0133] The transmission module is used to control each access point to transmit data by combining user data and the obtained precoding matrix.

[0134] The system of this embodiment and the above-mentioned method belong to the same inventive concept, which can be understood with reference to the above description and will not be described in detail here.

[0135] like Figure 4As shown, this embodiment also provides a computer device, including a processor 101 and a memory 102 connected by bus signals, wherein the memory 102 stores at least one instruction or at least one program, and the at least one instruction or at least one program is executed by the processor 101 when loaded to perform the precoding transmission method as described above. The memory 102 can be used to store software programs and modules, and the processor 101 executes various functional applications by running the software programs and modules stored in the memory 102. The memory 102 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, application programs required for functions, etc.; the data storage area can store data created according to the use of the device, etc. In addition, the memory 102 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 102 can also include a memory controller to provide the processor 101 with access to the memory 102.

[0136] The method embodiments provided in the embodiments of the present invention may be executed in a computer terminal, a server or a similar computing device, that is, the above-mentioned computer device may include a computer terminal, a server or a similar computing device. The internal structure of the computer device may include but is not limited to: a processor, a network interface and a memory. Among them, the processor, network interface and memory in the computer device may be connected via a bus or other means.

[0137] Among them, the processor 101 (or CPU (Central Processing Unit)) is the computing core and control core of the computer device. The network interface may optionally include a standard wired interface, a wireless interface (such as WI-FI, a mobile communication interface, etc.). The memory 102 (Memory) is a memory device in the computer device for storing programs and data. It can be understood that the memory 102 here can be a high-speed RAM storage device, or a non-volatile storage device (non-volatile memory), such as at least one disk storage device; optionally, it can also be at least one storage device located away from the aforementioned processor 101. The memory 102 provides a storage space, which stores the operating system of the electronic device, which may include but is not limited to: Windows system (an operating system), Linux (an operating system), Android (Android, a mobile operating system) system, IOS (a mobile operating system) system, etc., and the present invention is not limited to this; and, in the storage space, one or more instructions suitable for being loaded and executed by the processor 101 are also stored, and these instructions can be one or more computer programs (including program codes). In the embodiment of the present specification, the processor 101 loads and executes one or more instructions stored in the memory 102 to implement the precoding transmission method described in the above method embodiment.

[0138] The embodiment of the present invention further provides a computer-readable storage medium, on which at least one instruction or at least one program is stored, and the at least one instruction or at least one program is executed when loaded by the processor 101. The computer-readable storage medium carries one or more programs, and when the one or more programs are executed, the method according to the embodiment of the present invention is implemented.

[0139] According to an embodiment of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium. For example, it may include, but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device.

[0140] In the description of the present invention, it is necessary to understand that the directions or positional relationships indicated by directional words such as "front, back, up, down, left, right", "lateral, vertical, horizontal" and "top, bottom" are usually based on the directions or positional relationships shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description. Unless otherwise specified, these directional words do not indicate or imply that the device or element referred to must have a specific direction or be constructed and operated in a specific direction. Therefore, they cannot be understood as limiting the scope of protection of the present invention.

[0141] For those skilled in the art, various other corresponding changes and deformations can be made according to the technical solutions and concepts described above, and all of these changes and deformations should fall within the protection scope of the claims of the present invention.

Claims

1. A precoding transmission method for a non-cellular multi-antenna system, characterized in that: The following steps are involved: S01. Obtain channel spatial covariance information and channel noise power of the system; S02. Calculate a set of transmission characteristic matrices of the system according to the channel spatial covariance information, and iteratively optimize and obtain an optimal power allocation vector set of the system according to the channel spatial covariance information and noise power; S03. Calculate a precoding matrix for each access point based on the transmission characteristic matrix set and the optimal power allocation vector set; S04. Each access point combines the user data and the obtained precoding matrix to transmit data.

2. The precoding transmission method for a non-cellular multi-antenna system according to claim 1, characterized in that: In step S02, the sending feature matrix set of the computing system is specifically: The system includes M access points and one user terminal. The access point number i = 1 ~ M. Access point i is equipped with N T,i Root transmitting antennas, the user end is equipped with N R The channel spatial covariance information of the system is recorded as The noise power of the channel is Among them, Θ R,i represents the receiving spatial covariance matrix of the access point i to the user end channel, Θ T,i represents the transmission spatial domain covariance matrix of the access point i to the user end channel; According to the channel spatial covariance information For the transmitted spatial covariance matrix Θ T,i Perform eigenvalue decomposition: in, represents the transmission characteristic matrix of the i-th access point in the system, express The conjugate transpose operation of .

3. The precoding transmission method for a non-cellular multi-antenna system according to claim 2, characterized in that: In step S02, iterative optimization is performed to obtain the optimal power allocation vector set of the system according to the channel spatial covariance information and the noise power, and the specific steps include: S0201, initialization iteration index n = 0, auxiliary parameter μ component and the iteration step length t>0, where [μ1…μ M ] T Represents the set [μ1…μ M ], and calculating the initial value of the power allocation vector set according to the power allocation algorithm, wherein the power allocation algorithm comprises the following steps: a01. Initialize the loop index \(k = 0\) and the search step \(0 < S < 1\). Initialize the set \(B=\{b ij , i = 1\sim M, j = 1\sim4\}\), denoted as and satisfy the constraint where represents the maximum transmit power of access point \(i\); a02. Calculate the search direction set for the current iteration in: [M] ll It means taking the (l,l) element in the matrix M, l = 4(i-1) + j; is a 4M-dimensional column vector, defined as Represents a set The matrix transpose of ; blkdg(.) represents the block diagonalization operation of the matrix, E(.) represents the expectation of the channel matrix H, and diag(.) represents the diagonalization operation. Represents the matrix The conjugate transpose operation of ; H=[H1 H2...H M ], H i represents the channel matrix from access point i to the user end, in, i=1~M, g i represents the large-scale fading factor, H ω,i Indicates a dimension of N R ×N T,i A complex Gaussian random matrix, each element is independently counted and obeys a complex Gaussian distribution with a mean of 0 and a variance of 1; a03. Based on the obtained search direction set Update the set B: a04, if satisfied is the preset threshold, then the optimal solution of the set B is obtained, which is recorded as If not satisfied, increase the loop index k and return to step a02; a05. Calculate the power allocation vector set corresponding to the optimal solution of the set B As the initial value of the power allocation vector set: S202, updating the auxiliary parameter μ: in, represents the optimal solution of set B calculated by calling the power allocation algorithm for the nth time, P i Max Represents the power allocation vector set The maximum value of the elements in ; S203, according to the updated auxiliary parameters The power allocation algorithm is used to calculate and obtain an updated power allocation vector set as an iterative value of the power allocation vector set; S204: Based on the initial value and iterative value of the obtained power allocation vector set, determine whether convergence occurs. If convergence occurs, output the iterative value of the power allocation vector set as the optimal power allocation vector set, denoted as And end the loop; if not converged, update the iteration index n and return to step S202.

4. The precoding transmission method for a non-cellular multi-antenna system according to claim 3, characterized in that: The step S03 is specifically as follows: Calculate the precoding matrix W of each access point i : Based on the precoding matrix W of each access point i Construct a precoding matrix set {W i }.

5. The precoding transmission method for a non-cellular multi-antenna system according to claim 4, characterized in that: Step S04 includes the following steps: Each access point multiplies the user data by its own precoding matrix W i , synchronously transmit data.

6. A precoding transmission system, used to execute the method according to any one of claims 1 to 5, characterized in that: include: A measurement module, which is used to obtain the channel spatial covariance information and channel noise power of the system; An iterative optimization module, which is used to calculate a set of transmission characteristic matrices of the system according to the channel spatial covariance information, and iteratively optimize and obtain an optimal power allocation vector set of the system according to the channel spatial covariance information and noise power; A precoding matrix calculation module, which is used to calculate the precoding matrix of each access point based on the transmission characteristic matrix set and the optimal power allocation vector set; The transmission module is used to control each access point to transmit data by combining user data and the obtained precoding matrix.

7. A computer device comprising a processor and a memory connected by signals, characterized in that: The memory stores at least one instruction or at least one program, and when the at least one instruction or the at least one program is loaded by the processor, the precoding transmission method according to any one of claims 1 to 5 is executed.

8. A computer-readable storage medium having at least one instruction or at least one program stored thereon, characterized in that: When the at least one instruction or the at least one program is loaded by the processor, the precoding transmission method according to any one of claims 1 to 5 is executed.

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