Precoding matrix optimization method based on multi-user MIMO system

Abstract

The invention discloses a precoding matrix optimization method based on a multi-user MIMO system, and the method comprises the steps: obtaining a channel matrix through a base station channel estimation module, and defining a non-convex optimization problem of multi-user precoding; converting a non-convex optimization problem into a quantum annealing solvable secondary unconstrained binary optimization model, and dynamically reflecting a channel interference relationship through quantum bit coupling strength; designing a post-processing algorithm, compensating quantum hardware noise by using gradient descent and a nonlinear function of a power amplifier, and optimizing an output pre-coding matrix; the method overcomes the limitation that a traditional method is prone to falling into local optimum in non-convex optimization, can adapt to complex interference scenes in real time, and is suitable for a current 5G environment and a future wireless communication system.

Description

Technical Field

[0001] The present invention relates to the fields of wireless communication technology and quantum computing technology, and in particular to a precoding matrix optimization method based on a multi-user MIMO system. Background Art

[0002] With the rapid development of wireless communication technology, the requirements of mobile communication systems for spectrum efficiency and system capacity are increasing. Multi-User Multiple-Input Multiple-Output (MU-MIMO) technology configures multiple antennas at the transmitting and receiving ends, and uses spatial degrees of freedom to achieve simultaneous service for multiple users, effectively improving the spectrum efficiency and capacity of the system. It has become one of the key technologies in the fifth generation (5G) and future wireless communication systems. In MU-MIMO systems, precoding technology suppresses interference between users and improves channel capacity by appropriately processing the signal at the transmitting end, so it becomes an important means for MU-MIMO systems to achieve efficient transmission. At present, common precoding methods include Zero-Forcing (ZF) precoding, Minimum Mean Square Error (MMSE) precoding, and precoding methods based on convex optimization. However, with the increase in the number of users and the number of antennas, it is difficult for traditional precoding methods to achieve a good compromise between computational complexity and performance, especially when the optimization target involves non-convex problems, because traditional convex optimization methods often find it difficult to obtain a global optimal solution.

[0003] In recent years, the rise of quantum computing technology has provided new ideas for solving complex optimization problems. Among them, quantum annealing technology has attracted widespread attention due to its potential in solving combinatorial optimization problems. The quantum annealing algorithm constructs the Transverse Field Ising Model (TFIM) and utilizes the superposition state of quantum bits and the quantum tunneling effect, which is expected to efficiently solve non-convex optimization problems that are difficult to handle with traditional classical algorithms. However, the application of quantum annealing technology to the precoding optimization problem of MU-MIMO systems is still in its initial stage. Existing methods have not yet formed mature solutions in terms of how to effectively transform the precoding problem into a model that can be solved by quantum annealing, how to accurately map the channel interference relationship to the quantum bit coupling strength, and how to effectively compensate for quantum hardware noise. Therefore, it is urgent to propose a MU-MIMO precoding matrix optimization method based on quantum annealing to overcome the shortcomings of existing technologies in terms of computational complexity, optimization performance, and noise resistance. Summary of the invention

[0004] The purpose of this section is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this section, the abstract, and the title, and such simplifications or omissions shall not be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides a precoding matrix optimization method based on a multi-user MIMO system to solve the problems raised in the background art.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: A precoding matrix optimization method based on a multi-user MIMO system, comprising:

[0007] Obtain a channel matrix from the channel estimation module of the base station, and define a non-convex optimization problem for multi-user precoding through the channel matrix;

[0008] Convert the non-convex optimization problem of multi-user precoding into a quadratic unconstrained binary optimization model that can be solved by quantum annealing, and dynamically reflect the interference relationship of the channel through the qubit coupling strength;

[0009] Compensate for quantum hardware noise by designing a post-processing algorithm to optimize the output result of the model.

[0010] As a preferred solution of the precoding matrix optimization method based on a multi-user MIMO system according to the present invention, wherein: the channel estimation module includes:

[0011] The user equipment periodically sends an uplink sounding reference signal, and the uplink sounding reference signal is received by the radio frequency unit of the base station.

[0012] As a preferred solution of the precoding matrix optimization method based on a multi-user MIMO system according to the present invention, wherein: it further includes:

[0013] Based on the uplink sounding reference signal received by the radio frequency unit, calculate the channel response to obtain the channel vectors of all users;

[0014] Obtain a channel matrix according to the channel vectors of the users.

[0015] As a preferred solution of the precoding matrix optimization method based on a multi-user MIMO system according to the present invention, wherein: it further includes:

[0016] If the uplink sounding reference signal is lost during the calculation of the channel response, use the true channel vector of the previous frame as the current estimated channel vector.

[0017] As a preferred solution of the precoding matrix optimization method based on a multi-user MIMO system according to the present invention, wherein: a non-convex optimization problem of multi-user precoding is defined through the channel matrix, including:

[0018] The non-convex optimization problem is defined as minimizing the total interference and noise power received by each user under the precoding matrix, as well as maximizing the rate, and at the same time, the constraint conditions for minimizing the non-convex optimization problem are established.

[0019] As a preferred solution of the precoding matrix optimization method based on a multi-user MIMO system according to the present invention, wherein: the non-convex optimization problem of the multi-user precoding is transformed into a quadratic unconstrained binary optimization model that can be solved by quantum annealing, including:

[0020] Each element in the precoding matrix is discretized, and the non-convex optimization problem is transformed into a quadratic form to obtain the interference term and the noise term respectively;

[0021] The interference term and the noise term are combined to obtain the objective function of the non-convex optimization problem, and the constraint conditions of the non-convex optimization problem are transformed into penalty terms;

[0022] The penalty term is added to the objective function of the non-convex optimization problem to obtain the unconstrained form of the objective function.

[0023] As a preferred solution of the precoding matrix optimization method based on a multi-user MIMO system according to the present invention, wherein: it further includes:

[0024] According to the unconstrained form, a quadratic unconstrained binary optimization model is established, and the objective matrix of the quadratic unconstrained binary optimization model is obtained through the channel matrix, the noise power in the noise term, and the penalty term;

[0025] The quadratic unconstrained binary optimization model is transformed into a transverse-field Ising model of quantum annealing through variable transformation, and the interference term, the noise term, and the constraint conditions of the non-convex optimization problem are mapped to the coupling strength of the qubits in the transverse-field Ising model.

[0026] As a preferred solution of the precoding matrix optimization method based on a multi-user MIMO system according to the present invention, wherein: the interference relationship of the channel is dynamically reflected through the qubit coupling strength, including:

[0027] Considering the dynamic characteristics of the coupling strength of the qubits and the channel interference, the channel correlation is calculated, and the channel correlation is added to the coupling strength to update the original coupling strength;

[0028] The updated coupling strength is incorporated into the objective matrix of the quadratic unconstrained binary optimization model.

[0029] As a preferred solution of the precoding matrix optimization method based on the multi-user MIMO system according to the present invention, the following steps are included: compensating for quantum hardware noise by designing a post-processing algorithm, including:

[0030] By using the gradient descent method, the precoding matrix obtained by quantum annealing is corrected, and the precoding matrix obtained by quantum annealing is compensated by using the non-linear function of the power amplifier.

[0031] Compared with the prior art, the beneficial effects of the invention are as follows:

[0032] 1. The present invention transforms the non-convex precoding optimization problem of the multi-user MIMO system into a quadratic unconstrained binary optimization (QUBO) model that can be solved by quantum annealing. By utilizing the global optimization ability of quantum annealing, the limitation that the traditional method is prone to falling into local optimum is overcome.

[0033] 2. By dynamically mapping the channel interference relationship through the qubit coupling strength, the optimization model can be adjusted in real time according to the change of the channel matrix, enhancing the adaptability of the multi-user MIMO system to complex multi-user interference scenarios.

[0034] 3. Designing a post-processing algorithm to perform noise compensation and non-linear correction on the precoding matrix output by quantum annealing can improve the practicality of the solution, and at the same time solve the interference problem of quantum hardware noise on the optimization result, providing a reliable guarantee for the deployment of the actual multi-user MIMO system. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. Among them:

[0036] Figure 1 It is the overall flowchart of the precoding matrix optimization method based on the multi-user MIMO system according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the specific embodiments of the present invention in detail with reference to the accompanying drawings of the specification. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0038] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways than those specifically described herein. Those skilled in the art can make similar generalizations without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0039] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that exclude each other.

[0040] The present invention is described in detail in conjunction with schematic diagrams. When detailing the embodiments of the present invention, for ease of explanation, the cross-sectional views showing the device structure are enlarged locally in a non-general proportion, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention herein. In addition, in actual production, three-dimensional spatial dimensions of length, width, and depth should be included.

[0041] Meanwhile, in the description of the present invention, it should be noted that the orientation or positional relationships indicated by terms such as "upper, lower, inner, and outer" are based on the orientation or positional relationships shown in the drawings. This is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present invention. In addition, the terms "first, second, or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0042] Unless otherwise clearly defined and limited in the present invention, the terms "installed, connected, and coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, and may also be indirectly connected through an intermediate medium, or may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0043] Embodiment 1

[0044] Referring to Figure 1 , this is the first embodiment of the present invention. This embodiment provides a precoding matrix optimization method based on a multi-user MIMO system, including:

[0045] S1. Obtain a channel matrix from the channel estimation module of the base station, and define a non-convex optimization problem for multi-user precoding through the channel matrix;

[0046] It should be noted that in a wireless communication system, the channel estimation module is a functional component in a base station (BS) or a radio access node responsible for inferring the channel state information (CSI); it estimates the channel characteristics between the base station and the user equipment (UE) by analyzing the pilot signals or reference signals sent by the user equipment (UE), and generates a channel matrix;

[0047] Furthermore, the working process of the channel estimation module is as follows:

[0048] The user equipment periodically sends uplink sounding reference signals, which are known orthogonal sequences. The uplink sounding reference signals are received by the radio frequency unit of the base station and converted into digital signals;

[0049] Based on the uplink sounding reference signals received by the radio frequency unit, calculate the channel response to obtain the channel vectors of all users;

[0050] Specifically, the formula for calculating the channel response is expressed as:

[0051]

[0052] Among them, represents the estimated channel vector of the nth user, h n represents the true channel vector of the nth user; X represents the known orthogonal sequence, y n represents the received nth uplink sounding reference signal, is the fitting error, expressed as the deviation between h n and y n ; represents the regularization term, which restricts the magnitude (norm) of h n ; λ represents the penalty coefficient, which is used to control the fitting error and the regularization term. When λ = 0, only the fitting error is optimized, which is equivalent to the least squares (LS) estimation. When λ > 0, the regularization term penalizes an overly large h n , preventing the estimation result from being too complex or unstable;

[0053] According to the channel vectors of the users, obtain the channel matrix;

[0054] Specifically, the channel matrix is expressed as:

[0055]

[0056] Among them, T is the transpose matrix, and K is the total number of users;

[0057] Specifically, for the obtained channel matrix, assume that the base station has N t antennas and serves K users. Then, this channel matrix is denoted as which represents the channel state from the base station to the users. The true channel vector h of each row of users n , is represented as the set of complex numbers;

[0058] Furthermore, if the uplink sounding reference signal is lost during the calculation of the channel response, the true channel vector of the previous frame is used as the current estimated channel vector;

[0059] Even further, verify the dimension (K×N t ) and numerical validity (absence of NaN or Inf) of the channel matrix. If either the dimension or the numerical validity does not meet the requirements, recalculate the channel response;

[0060] It should be noted that since the base station needs to transmit data to multiple users (UEs) simultaneously, in order to improve the spectral efficiency and system capacity, the base station generally uses precoding technology to adjust the transmitted signal, maximizing the signal of each user at the receiving end while minimizing the interference between users. This optimization problem is usually non-convex;

[0061] Specifically, define the multi-user precoding matrix as

[0062] It should be noted that by optimizing this multi-user precoding matrix, each user can receive its own signal while reducing the interference from other users' signals;

[0063] Furthermore, define the non-convex optimization problem of multi-user precoding as minimizing the total interference and noise power received by each user under the precoding matrix, as well as maximizing the rate, and simultaneously establish the constraint conditions for minimizing the non-convex optimization problem;

[0064] It should be noted that a non-convex problem refers to an optimization problem whose domain of the objective function or constraint conditions is not a convex set, or the objective function is not a convex function;

[0065] Specifically, minimize the total interference and noise power received by each user under the precoding matrix, while satisfying the following power constraint:

[0066]

[0067] Subject to the constraint:

[0068]

[0069] where, H n W -nrepresents the interference of the precoding signals of other users received by the user; σ 2 is denoted as the noise power; represents the impact of noise on the user; P max is the maximum transmit power; is the total power of the precoding matrix;

[0070] Specifically, rate maximization refers to optimizing the sum of the data transmission rates of each user, obtaining:

[0071]

[0072] where, w n and w m are both columns in the precoding matrix, denoted as the precoding vectors designed for the users;

[0073] It should be noted that since is a quadratic function of precoding, but there are cross - calculations between different users, so the optimization objective is non - convex; and the constraint conditions are quadratic constraints, belonging to non - convex; in addition, for the optimization objective of rate maximization, it can be seen that both the numerator and denominator depend on W and are non - convex; this non - convexity makes traditional optimization methods (especially gradient descent) prone to falling into local optima and unable to guarantee the global optimal solution;

[0074] S2. Transform the non - convex optimization problem of the multi - user precoding into a quadratic unconstrained binary optimization model that can be solved by quantum annealing, and dynamically reflect the interference relationship of the channel through the qubit coupling strength;

[0075] It should be explained that although quantum annealing can provide an efficient means to solve non - convex problems in combinatorial optimization problems, it can only handle objective functions in quadratic form and the variables must be binary (0 or 1). Therefore, it is also necessary to transform the non - convex optimization problem of multi - user precoding into a quadratic unconstrained binary optimization (QUBO) model;

[0076] Furthermore, discretize each element in the precoding matrix and transform the non - convex optimization problem into a quadratic form to obtain the interference term and the noise term respectively;

[0077] Specifically, discretize each element w nm in the precoding matrix to obtain:

[0078]

[0079] where, b represents the number of quantization bits, k is the index of the number of quantization bits, j represents the imaginary unit, and represent the binary bits of the real part and the imaginary part respectively, and their range is denoted as In addition, each element w in the precoding matrix nm requires 2×b binary vectors (b for both the real part and the imaginary part), and the precoding matrix W has N t ×K elements, resulting in a total number of binary vectors D = 2bNK; t

[0080] Exemplarily, if w 11 = 3 + 2j and b = 4; then the real part 3 = 0011 (binary), corresponding to while the imaginary part 2 = 0010 (binary), corresponding to

[0081] Specifically, rewrite to obtain the interference term:

[0082]

[0083] where the noise term:

[0084]

[0085] Furthermore, merge the interference term and the noise term to obtain the objective function of the non-convex optimization problem, and transform the constraint conditions of the non-convex optimization problem into penalty terms;

[0086] Specifically, the objective function of the non-convex optimization problem after merging is expressed as:

[0087]

[0088] where f(x) represents the total objective function of the non-convex optimization problem;

[0089] Specifically, transform the constraint conditions of the non-convex optimization problem into penalty terms:

[0090]

[0091] where

[0092] Furthermore, add the penalty term to the objective function of the non-convex optimization problem to obtain the unconstrained form of the objective function;

[0093] Specifically, the unconstrained form of the objective function is expressed as:

[0094] min f(x)+P(x)

[0095] ​Furthermore, according to the unconstrained form, a quadratic unconstrained binary optimization model is established, and the objective matrix of the quadratic unconstrained binary optimization model is obtained through the channel matrix, the noise power in the noise term, and the penalty term.

[0096] Specifically, the quadratic unconstrained binary optimization model is established as follows:

[0097] min x T Qx

[0098] where Q is a D×D symmetric matrix, and x is a binary vector of length D.

[0099] Specifically, the construction of Q is determined by expanding f(x)+P(x), extracting all quadratic term coefficients and linear term coefficients, and through the channel matrix, the noise power in the noise term, and the penalty term.

[0100] Furthermore, the quadratic unconstrained binary optimization model is transformed into a transverse-field Ising model of quantum annealing through variable transformation, and the interference term, the noise term, and the constraint conditions of the non-convex optimization problem are mapped to the coupling strength of the qubits in the transverse-field Ising model.

[0101] Specifically, the QUBO model is transformed into the form of the transverse-field Ising model TLM through variable transformation:

[0102]

[0103] where R is a constant; Q nm represents the matrix element of the QUBO model, which determines the linear term coefficient and the quadratic term coefficient; x n ,x m both represent variable transformations.

[0104] Specifically, quantum annealing is based on the transverse-field Ising model, and its Hamiltonian H TLM is expressed as:

[0105]

[0106] where represents the spin of the nth qubit (corresponding to the binary variable, taking values of +1 or -1); g n represents the local magnetic field of the qubit (linear term coefficient); J nm represents the coupling strength between the nth and the mth qubits (quadratic term coefficient).

[0107] It should be noted that through the quantum annealing process, the model can converge to the ground state of H TLM corresponding to the global optimal solution of the optimization problem.

[0108] Furthermore, in order to make the coupling strength between qubits better reflect the dynamic characteristics of channel interference, by considering the coupling strength of qubits and the dynamic characteristics of channel interference, the channel correlation is calculated, and the channel correlation is added to the coupling strength to update the original coupling strength;

[0109] Specifically, the channel correlation C nm is calculated as follows:

[0110]

[0111] where represents the inner product of the channel vectors of users n and m;

[0112] It should be noted that the greater the channel correlation, the more similar the channels of users n and m are, and the stronger the channel interference is;

[0113] Specifically, adding the channel correlation to the coupling strength gives:

[0114] J nm = α × C nm + β × δ nm

[0115] where α is the channel interference weight coefficient, used to control the influence of channel correlation on the coupling strength, β is the self-coupling coefficient, used to adjust the independence of qubits; δ nm is expressed as the Kronecker function, which is 1 when n = m and 0 otherwise, and is used to enhance the diagonal terms;

[0116] Furthermore, the updated coupling strength is incorporated into the target matrix of the quadratic unconstrained binary optimization model;

[0117] Specifically, by using J nm to adjust Q, we get:

[0118] Q ∝ J nm

[0119] It should be noted that by calculating all J nm , the influence of J nm is distributed to the corresponding elements of Q, thereby updating the original coupling strength, decoding to obtain the precoding matrix, and realizing the process of quantum annealing optimization of the precoding matrix;

[0120] S3. Compensate for quantum hardware noise by designing a post-processing algorithm to optimize the output result of the model;

[0121] It should be noted that after solving the QUBO model by quantum annealing, due to the physical limitations of the quantum hardware itself (such as noise, decoherence, bit flip errors, etc.), the solution result may deviate from the theoretical optimal solution. Therefore, it is necessary to correct or optimize the result output by quantum annealing through a post-processing algorithm to improve the practicality of the solution;

[0122] Furthermore, through the gradient descent method, the precoding matrix obtained by quantum annealing is corrected, and the precoding matrix obtained by quantum annealing is compensated using the non-linear function of the power amplifier;

[0123] Specifically, through the gradient descent method for fine-tuning, we get:

[0124]

[0125] where W * represents the precoding matrix decoded by quantum annealing, η represents the learning rate of gradient descent, and W final is the final value obtained after fine-tuning by the gradient descent method, represents the gradient;

[0126] It should be noted that the quantum annealing result is corrected by the gradient descent method to compensate for the negative impact of hardware noise on the solution;

[0127] It should be noted that in a wireless communication system, the power amplifier is mainly used to amplify the power of the base station transmitted signal to ensure the signal coverage range and intensity; in an ideal situation, the amplifier is linear and the output signal is proportional to the input signal. However, in actual situations, the power amplifier will exhibit non-linearity in the high-power region, resulting in output signal distortion. Therefore, it is necessary to compensate for the non-linear distortion of the power amplifier (Power Amplifier, PA) to further optimize the precoding matrix to make it more suitable for signal transmission in an actual communication system;

[0128] Specifically, the non-linear function of the power amplifier is used to compensate for W final to get:

[0129] W out = u - 1(W final )

[0130] where u -1 (·) represents the non-linear function of the power amplifier, and W out is the compensation value.

[0131] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript, etc.

[0132] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0133] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0134] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0135] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present application.

[0136] Obviously, those skilled in the art can 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 equivalent technologies, this application is also intended to cover these changes and modifications.

Claims

1. A precoding matrix optimization method based on a multi-user MIMO system, characterized in that Including: Obtain a channel matrix from the channel estimation module of a base station, and define a non-convex optimization problem of multi-user precoding through the channel matrix; Convert the non-convex optimization problem of multi-user precoding into a quadratic unconstrained binary optimization model that can be solved by quantum annealing, and dynamically reflect the interference relationship of the channel through the qubit coupling strength; Compensate for quantum hardware noise by designing a post-processing algorithm to optimize the output result of the model.

2. The precoding matrix optimization method based on a multi-user MIMO system according to claim 1, wherein The channel estimation module includes: User equipment periodically sends uplink sounding reference signals, and the uplink sounding reference signals are received through the radio frequency unit of the base station.

3. The precoding matrix optimization method based on a multi-user MIMO system according to claim 2, wherein It also includes: Based on the uplink sounding reference signals received by the radio frequency unit, calculate the channel response to obtain the channel vectors of all users; Obtain the channel matrix according to the channel vectors of the users.

4. The precoding matrix optimization method based on a multi-user MIMO system according to claim 2, characterized in that It also includes: If the uplink sounding reference signal is lost during the process of calculating the channel response, use the true channel vector of the previous frame as the current estimated channel vector.

5. The precoding matrix optimization method based on a multi-user MIMO system according to claim 4, wherein Defining a non-convex optimization problem of multi-user precoding through the channel matrix includes: The non-convex optimization problem is defined as minimizing the total interference and noise power received by each user under the precoding matrix, as well as maximizing the rate, and at the same time establishing the constraint conditions for minimizing the non-convex optimization problem.

6. The precoding matrix optimization method based on a multi-user MIMO system according to claim 5, wherein, Converting the non-convex optimization problem of multi-user precoding into a quadratic unconstrained binary optimization model that can be solved by quantum annealing includes: Discretize each element in the precoding matrix, and convert the non-convex optimization problem into a quadratic form to obtain the interference term and the noise term respectively; Combine the interference term and the noise term to obtain the objective function of the non-convex optimization problem, and convert the constraint conditions of the non-convex optimization problem into penalty terms; Add the penalty term to the objective function of the non-convex optimization problem to obtain the unconstrained form of the objective function.

7. The precoding matrix optimization method based on a multi-user MIMO system according to claim 2 or 6, characterized in that It also includes: According to the unconstrained form, establish a quadratic unconstrained binary optimization model, and obtain the objective matrix of the quadratic unconstrained binary optimization model through the channel matrix, the noise power in the noise term, and the penalty term; Convert the quadratic unconstrained binary optimization model into a transverse-field Ising model of quantum annealing through variable transformation, and map the interference term, the noise term, and the constraint conditions of the non-convex optimization problem to the coupling strength of the qubits in the transverse-field Ising model.

8. The precoding matrix optimization method based on a multi-user MIMO system according to claim 7, wherein, Dynamically reflecting the interference relationship of the channel through the qubit coupling strength includes: Considering the dynamic characteristics of the qubit coupling strength and the channel interference, calculate the channel correlation, and add the channel correlation to the coupling strength to update the original coupling strength; Integrate the updated coupling strength into the objective matrix of the quadratic unconstrained binary optimization model.

9. The precoding matrix optimization method based on a multi-user MIMO system according to claim 5 or 8, characterized in that Compensating for quantum hardware noise by designing a post-processing algorithm includes: Through the gradient descent method, correct the precoding matrix obtained by quantum annealing solvability, and compensate the precoding matrix obtained by quantum annealing solvability using the non-linear function of the power amplifier.

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