CN117439638A - Low-complexity WSR optimization method, device and medium for non-ideal de-honeycomb large-scale MIMO system - Google Patents

Abstract

The invention discloses a non-ideal cellular massive MIMO system low-complexity WSR optimization method, device and medium, including: Step 1. Considering low-precision ADC quantization distortion, establish an uplink pilot training model, and derive the Ricean fading model The MMSE channel estimation expression of The secondary transformation and Lagrangian dual methods convert the original optimization problem into a convex problem, and design a low-complexity method that can solve the convex problem; Step 4: Dynamically adjust the UE transmit power according to the power coefficient to achieve the WSR optimization goal. The optimization method designed by the present invention can compensate for WSR distortion caused by non-ideal hardware by optimizing the power control coefficient. And compared with the existing WSR optimization algorithm, the method of the present invention greatly reduces the time complexity and has fast running speed on the premise of achieving the same WSR gain.

Description

A low-complexity WSR optimization method and device for non-ideal cellular massive MIMO systems Setup and media

Technical field

The invention relates to the field of wireless communications, and specifically relates to a low-complexity WSR optimization method, device and medium for a non-ideal cellular massive MIMO system.

Background technique

Compared with massive MIMO (Multiple-Input Multiple-Output) technology based on cellular architecture, decellularized massive MIMO technology distributes base station antenna units and serves multiple user equipment (User) simultaneously on the same time-frequency resources. Equipment, UE), eliminates inter-cell interference and reduces the path loss between the Access Point (AP) and the UE, greatly improving the rate performance of the cell edge UE. In view of its advantages such as high speed, high macro gain and uniform coverage quality, decellularized massive MIMO has been regarded as a key physical layer transmission technology in future 6G communication systems.

In order to meet the high traffic demand of a large number of UEs, decellularized massive MIMO systems need to deploy more and more APs, which causes the deployment cost and hardware power consumption to increase dramatically. In order to solve the above problems, non-ideal hardware can be used in decellularized massive MIMO systems, such as low-precision Analog-to-Digital Converter (ADC) and Digital-to-Analog Converter (DAC). ), but this will introduce quantization noise and reduce UE rate performance. In order to compensate for the performance loss caused by low-precision components and improve system performance, effective power control algorithms can be designed, such as the Weighted Sum Rate (WSR) optimization algorithm. Although there are existing literatures that have designed WSR optimization algorithms, these literatures all use the Successive Convex Approximation (SCA) method, which rewrites the original optimization problem into a specific convex programming problem, and then uses the interior point method to solve the convex programming problem. Although this type of method can improve the system WSR, its computational complexity is high and its running speed is slow, making it unsuitable for use in large-scale decellularized massive MIMO systems.

Therefore, there is an urgent need to design a low-complexity and fast-running WSR optimization method in order to compensate for the rate loss caused by low-precision hardware and improve system rate performance.

Contents of the invention

The purpose of the present invention is to overcome the deficiencies in the existing technology and provide a low-complexity WSR optimization method, device and medium for non-ideal cellular massive MIMO systems. This method can better compensate for the rate loss caused by non-ideal hardware. Significantly improves the WSR performance of non-ideal cellular massive MIMO systems.

In order to achieve the above objects, the present invention is achieved by adopting the following technical solutions:

In the first aspect, the present invention provides a low-complexity WSR optimization method for non-ideal cellular massive MIMO systems, which includes the following steps:

Step 1. Considering the low-precision ADC quantization distortion, establish an uplink pilot training model and obtain the MMSE channel estimation expression under the Ricean fading model;

Step 2. Considering the low-precision ADC and DAC quantization distortion, use the MRC receiver and UatF technology to obtain the closed expression of the UE rate;

Step 3. Based on the MMSE channel estimation expression and the UE rate closed expression, establish a WSR optimization problem related to the power control coefficient, and convert the original optimization problem into a convex problem based on the quadratic transformation and Lagrange duality method. Design a low-complexity method that can solve the convex problem in a closed form and obtain the power coefficient;

Step 4: Dynamically adjust the UE transmit power according to the power coefficient to achieve the WSR optimization goal.

Further, step 1 includes:

After the Central Processing Unit (CPU) allocates pilot signals to UEs, it is assumed that all UEs transmit pilot signals to the AP at the same time. After the pilot signal received by AP _m is quantized by low-resolution ADC, it can be modeled as

Among them, m = 1, 2, ..., M, M is the total number of APs, k = 1, 2, ..., K, K is the total number of UEs, is a set composed of UE subscripts, τ is the pilot length, ρ _p is the pilot transmission power of UE _k , g _mk is the Ricean fading channel vector between AP _m and the k-th UE, that is, the Ricean fading channel vector between UE _k , /> represents the pilot signal, the superscript H represents the conjugate transpose,/> is the Gaussian white noise matrix, N is the number of antennas of the AP,/> It is a complex domain, α _m represents the distortion factor that measures the degree of ADC distortion, Y _{m, p} represents the pilot signal before quantization, /> is the quantization noise signal.

based on And using MMSE estimation technology, the MMSE estimate of channel g _mk is:

in, Represents the subscript set of all UEs using the same pilot as UE _k , j is the subscript of other UEs different from k, β _mk is the equivalent large-scale fading coefficient between AP _m and UE _k , β _mj is AP _m The equivalent large-scale fading coefficient between UE j and UE _j , σ ² is the noise power.

Further, step 2 includes:

Considering the quantization distortion of low-precision ADC and DAC, an uplink data transmission model is established based on the MRC receiver. The data signal received by the CPU can be modeled as

Among them, λ _m represents the distortion factor that measures the degree of DAC distortion, is the DAC quantization noise at AP _m , ρ _u is the data transmission power of UE, η _j represents the power control coefficient of UE _j , q _j represents the data signal of UE _j , n _m is the Gaussian white noise at AP _m , Then represents the ADC quantization noise at AP _m . Using UatF technology for the above equation, it can be deduced that the closed-form expression of the rate lower bound of UE _k is/> in

In the above formula, K _mk and K _mj represent the Rice K factor, γ _mk and γ _mj represent the channel estimation quality factor, θ _mk represents the arrival incident angle between UE _k and AP _m , θ _mj is UE _j and AP _m angle of arrival.

Further, step 3 includes:

The optimization problem with maximizing WSR as the goal and using the power control coefficient of the UE as the independent variable is modeled as

Among them, w _k represents the rate weight of UE _k , Indicates the upstream WSR. Constraint 1 stipulates that the actual transmit power of the UE should be greater than or equal to 0 and cannot exceed its maximum transmit power.

For optimization problems The Lagrangian dual transformation technique is used for it,/> Can be equivalently converted to

Among them, ξ _k represents the newly added auxiliary variable. For /(η _k , ξ _k ), find the first-order partial derivative with respect to ξ _k and let it be 0, we can get ξ _k as Will/> After that, the optimization problem/> Equivalent to

for optimization problems Perform a second transformation, which is equivalent to

Among them, δ _k represents the newly added auxiliary variable. Find the first-order partial derivative of g(η _k , δ _k ) with respect to δ _k and let it be 0, we can get δ _k as

in, Will/> Substitute optimization problem/> Solve/> Regarding the first-order partial derivative of eta _k and letting it be 0, the power control coefficient can be obtained as

Based on the above update function, the optimal value of the power control coefficient can be obtained through several iterations.

Specifically, the detailed steps of the proposed WSR optimization algorithm are:

Step1: Initialize the number of iterations i←0, define the tolerance error ε>0, and set the initial feasible solution as Calculate the initial WSR, denoted as/>

Step2: Update for/>

Step3: Update for/>

Step4: Update for/>

StepS: Based on Update/>

Step6: Judgment whether it is established;

Step7: If it is not established, let i←i+1 and repeat steps Step2-Step5;

Step8: If established, let then/> That is the original WSR optimization problem/> a suboptimal solution.

In a second aspect, the present invention provides a low-complexity WSR optimization device for non-ideal cellular massive MIMO systems. The device includes:

Channel estimation module: used to consider low-precision ADC quantization distortion, establish an uplink pilot training model, and obtain the MMSE channel estimation expression under the Ricean fading model;

UE rate module: used to consider low-precision ADC and DAC quantization distortion, and use MRC receiver and UatF technology to obtain the closed expression of UE rate;

Solving module: used to establish a WSR optimization problem related to the power control coefficient based on the MMSE channel estimation expression and the UE rate closed expression, and convert the original optimization problem into a convex one based on the quadratic transformation and Lagrange duality method. problem, design a low-complexity method that can solve the convex problem in a closed form, and obtain the power coefficient;

Adjustment module: used to dynamically adjust the UE transmit power according to the power coefficient to achieve WSR optimization goals.

In a third aspect, the present invention provides a low-complexity WSR optimization device for non-ideal cellular massive MIMO systems, including a processor and a storage medium;

The storage medium is used to store instructions;

The processor is configured to operate according to the instructions to perform the steps of the method according to the first aspect.

In a fourth aspect, the present invention provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the method described in the first aspect are implemented.

Compared with the prior art, the beneficial effects achieved by the present invention are:

This invention studies the impact of low-precision ADC and DAC on the WSR performance of cellular massive MIMO system under Ricean fading channel, and uses AQNM and MRC receivers to derive MMSE channel estimation and rate closed-form expression based on UatF; then, considering UE transmission For power constraints, design a WSR optimization scheme to compensate for the WSR performance loss caused by low-precision components by optimizing the UE transmit power coefficient.

In addition, compared with the existing WSR optimization method, the WSR optimization method designed by the present invention can iteratively update the power coefficient based on a closed expression, greatly reducing the time complexity while achieving the same WSR gain, and the operation speed is fast, so It has extensive use value and application prospects.

Description of the drawings

Figure 1 is a flow chart of the WSR optimization method according to the embodiment of the present invention;

Figure 2 is a relationship diagram between system WSR and ADC/DAC accuracy according to the embodiment of the present invention;

Figure 3 is a comparison diagram of the running time between the WSR optimization method described in the embodiment of the present invention and the existing WSR optimization method.

Detailed ways

The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to more clearly illustrate the technical solutions of the present invention, but cannot be used to limit the scope of the present invention.

Example 1:

This embodiment proposes a low-complexity WSR optimization method for non-ideal cellular massive MIMO systems. The designed WSR optimization method has the advantage of low complexity and can dynamically adjust the actual transmit power of each UE, thereby achieving The purpose of compensating WSR performance distortion caused by low-precision components and optimizing WSR.

This method includes:

Step 1. Considering the low-precision ADC quantization distortion, establish an uplink pilot training model and derive the MMSE channel estimation expression under the Ricean fading model.

Under the Ricean fading channel model dominated by Line of Sight (LoS) propagation, an uplink pilot training model is established based on the Additive Quantization Noise Model (AQNM). The AP And the MMSE estimation method is used to derive the channel estimate including quantization noise.

The system includes AP, users and CPU, and all optimizations are calculated on the CPU side. After the CPU calculates the power coefficient, it forwards it to the UE, and the UE dynamically adjusts its transmit power.

The present invention studies an uplink cellular-scale MIMO system. The system includes M APs, K UEs and 1 CPU. Each AP is equipped with N antennas, and each UE is equipped with a single antenna. The wireless channel between AP and UE is modeled based on Ricean fading channel. Therefore, the N×1-dimensional channel vector between AP _m and UE _k can be expressed as

Among them, ξ _mk represents the large-scale fading coefficient between AP _m and UE _k , is the LoS component,/> is the non-LoS component, and K _mk is the Rice K factor. Before pilot training, the CPU uses a specific pilot allocation scheme as/> Assign pilot sequence/> Among them/> Represents the complex domain, τ is the pilot length. Assuming that K UEs simultaneously transmit pilot signals to the AP with maximum transmission power ρ _p , the pilot vector received by AP _m can be expressed as

Among them, m = 1, 2, ..., M, k = 1, 2, ..., K, κ = {1, 2, ..., K} is a set composed of UE subscripts and superscripts H represents conjugate transpose, is a Gaussian white noise matrix, α _m represents the distortion factor that measures the degree of ADC distortion, Y _{m, p} represents the pilot signal before quantization, /> is the quantization noise signal. Based on/> And using MMSE estimation technology, the MMSE of channel g _mk is estimated as

in, Represents the subscript set of all UEs using the same pilot as UE _k , j is the subscript of other UEs different from k, β _mk is the large-scale fading coefficient between AP _m and UE _k , β _mj is AP _m and UE The large-scale fading coefficient between _j , σ ² is the noise power.

Step 2. Considering the quantization distortion of low-precision ADC and DAC, establish an uplink data transmission model based on the MRC receiver, and use UatF technology to derive the closed expression of the UE rate.

Based on AQNM, low-resolution ADC and DAC distortions are modeled, and the expression of the data signal received by the AP is established. The AP builds an MRC receiver based on the estimated uplink CSI (Channel State Information) for signal detection, and uses UatF technology to derive a closed expression of the UE rate.

Let q _k represent the data signal of UE _k . When all UEs transmit data signals to the AP at the same time, the data signal received by AP _m can be expressed as

Among them, ρ _u is the data transmission power of UE, eta _k represents the power control coefficient of UE _k , n _m is the Gaussian white noise at AP _m , Then represents the ADC quantization noise at AP _m . To detect the signal q _k , an MRC receiver scheme is assumed. AP _m based on/> Right/> Despread, and the despread signal is quantized by a low-precision DAC and forwarded to the CPU. The data signal received by the CPU can be modeled as

Among them, λ _m represents the distortion factor that measures the degree of DAC distortion, is the DAC quantization noise at AP _m . Using UatF technology for the above equation, it can be deduced that the closed-form expression of the rate lower bound of UE _k is/> in

In the above formula, K _mk and K _mj represent the Rice K factor, γ _mk and γ _mj represent the channel estimation quality factor, θ _mk represents the arrival incident angle between UE _k and AP _m , θ _mj is UE _j and AP _m angle of arrival.

Step 3. Establish a WSR optimization problem related to the power control coefficient. Based on the quadratic transformation and Lagrangian duality method, convert the original optimization problem into a convex problem, and design a low-complexity WSR that can solve the optimal power control coefficient in a closed form. Optimization

Taking the UE's transmit power limit as a constraint, a rate weighting factor is introduced, a WSR optimization problem related to the UE power control coefficient is established and the problem is solved. The original optimization problem is equivalently converted into a convex problem using Lagrangian dual transformation and quadratic transformation respectively. On this basis, the optimal power control coefficient is iteratively updated based on the closed-form expression, and finally the purpose of optimizing WSR is achieved.

Considering the UE transmit power constraint, the optimization problem with maximizing WSR as the goal and the UE power control coefficient as the independent variable is modeled as

Among them, w _k represents the rate weight of UE _k , Indicates the upstream WSR. Constraint 1 stipulates that the actual transmit power of the UE should be greater than or equal to 0 and cannot exceed its maximum transmit power. It’s not difficult to find optimization problems/> It is strictly non-convex and it is difficult to find its optimal solution in polynomial time. For this problem, it can be converted into a convex optimization problem with the help of Lagrangian dual transformation and quadratic transformation methods, so that a suboptimal solution to the original problem can be obtained by iteratively solving the problem. According to the Lagrangian dual transformation, by introducing auxiliary variables ξ _k , the optimization problem/> Can be equivalently converted to

It is not difficult to find that when eta _k is given, f(eta _k , ξ _k ) is convex and differentiable with respect to ξ _k , so ξ _k can be obtained by solving its first derivative and letting it be 0. Find the first-order partial derivative of f(η _k , ξ _k ) with respect to ξ _k and let it be 0, we can get ξ _k as Will/> After substituting f(η _k , ξ _k ), the optimization problem/> Equivalent to

Optimization Still a non-convex optimization problem. For this purpose, quadratic transformation technology is used to optimize the problem/> Can be equivalently converted to

Among them, δ _k represents the newly added auxiliary variable. It is not difficult to find that when eta _k is given, g(η _k , δ _k ) is convex and differentiable with respect to δ _k , so δ _k can be obtained by solving its first derivative and letting it be 0. Find the first-order partial derivative of g(η _k , δ _k ) with respect to δ _k and let it be 0, we can get δ _k as

in, Will/> Substitute optimization problem/> Finally, it can be found that the objective function g(η _k , δ _k ) is convex and differentiable with respect to η _k . So solve/> Regarding the first-order partial derivative of eta _k and letting it be 0, the power control coefficient can be obtained as

Based on the above update function, the optimal value of the power control coefficient can be obtained through several iterations.

Specifically, the detailed steps of the proposed WSR optimization algorithm are:

Step1: Initialize the number of iterations i←0, define the tolerance error ε>0, and set the initial feasible solution as Calculate the initial WSR, denoted as/>

Step2: Update for/>

Step3: Update for/>

Step4: Update for/>

Step5: Based on Update/>

Step6: Judgment whether it is established;

Step7: If it is not established, let i← i+1 and repeat steps Step2-Step5;

Step8: If established, let then/> That is the original WSR optimization problem/> a suboptimal solution.

Step 4: Dynamically adjust the UE transmit power according to the power coefficient to achieve the WSR optimization goal.

The performance of the technical solution of the present invention will be further explained below in combination with simulation experiments.

Figure 2 shows the relationship between upstream WSR and ADC/DAC accuracy, where the abscissa is the ADC/DAC resolution and the ordinate is the upstream WSR. In order to compare with the WSR optimization method designed by the present invention, the full power transmission scheme And the uplink WSR under the traditional geometric programming-based WSR optimization method is also shown in Figure 2. The simulation parameters are set to M=25/36, K=10, N=6,/> σ ² =-126dBm, ρ _p =ρ _u =20dBm, w _k =1, ε = 10 ^-2 , and the simulation scene is an area of 400m×400m. As shown in Figure 2, for decellularized massive MIMO systems with M=36 and M=25, WSR increases with the increase of ADC/DAC resolution and tends to be saturated near 5 bits, indicating that it can be achieved by using 5- bit ADC and DAC to reduce hardware power consumption and deployment costs, and achieve the same WSR performance as high-precision ADC and DAC. Compared with the full-power transmission scheme, the optimization method proposed by the present invention can significantly improve WSR performance. In addition, the WSR under the optimization method proposed in the present invention is almost consistent with the WSR under the optimization method based on geometric programming.

Figure 3 shows the relationship between the running time of different optimization algorithms and the number of UEs. Observing Figure 3, it can be found that compared with the WSR optimization method based on geometric programming, the running time required by the WSR optimization method proposed in the present invention is reduced. Even in a decellularized massive MIMO system with a large number of UEs, such as K≥22, the running time required by the optimization method proposed in the present invention does not exceed 0.1 seconds. Combining the conclusions obtained in Figures 2 and 3, it can be found that the WSR optimization method designed in the present invention can significantly improve WSR and significantly reduce running time, so it has high practicability.

Example 2:

This embodiment provides a low-complexity WSR optimization device for non-ideal cellular massive MIMO systems. The device includes:

Channel estimation module: used to consider low-precision ADC quantization distortion, establish an uplink pilot training model, and obtain the MMSE channel estimation expression under the Ricean fading model;

UE rate module: used to consider low-precision ADC and DAC quantization distortion, and use MRC receiver and UatF technology to obtain the closed expression of UE rate;

Solving module: used to establish a WSR optimization problem related to the power control coefficient based on the MMSE channel estimation expression and the UE rate closed expression, and convert the original optimization problem into a convex one based on the quadratic transformation and Lagrange duality method. problem, design a low-complexity method that can solve the convex problem in a closed form, and obtain the power coefficient;

Adjustment module: used to dynamically adjust the UE transmit power according to the power coefficient to achieve WSR optimization goals.

The device of this embodiment can be used to implement the method described in Embodiment 1.

Embodiment three:

This embodiment provides a low-complexity WSR optimization device for non-ideal cellular massive MIMO systems, including a processor and a storage medium;

The storage medium is used to store instructions;

The processor is configured to operate according to the instructions to perform the steps of the method according to Embodiment 1.

Embodiment 4:

This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the method described in Embodiment 1 are implemented.

Those skilled in the art will understand that embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment that combines software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each process and/or block in the flowchart illustrations and/or block diagrams, and combinations of processes and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device produce a use A device for realizing the functions specified in one process or multiple processes of the flowchart and/or one block or multiple blocks of the block diagram.

These computer program instructions may also be stored in a computer-readable memory that causes a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including the instruction means, the instructions The device implements the functions specified in a process or processes of the flowchart and/or a block or blocks of the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device, causing a series of operating steps to be performed on the computer or other programmable device to produce computer-implemented processing, thereby executing on the computer or other programmable device. Instructions provide steps for implementing the functions specified in a process or processes of a flowchart diagram and/or a block or blocks of a block diagram.

The above are only preferred embodiments of the present invention. It should be noted that those of ordinary skill in the art can also make several improvements and modifications without departing from the technical principles of the present invention. These improvements and modifications It should also be regarded as the protection scope of the present invention.

Claims (8)

1. A low-complexity WSR optimization method for non-ideal cellular massive MIMO systems, which is characterized by including the following steps: Step 1. Considering the low-precision ADC quantization distortion, establish an uplink pilot training model and obtain the MMSE channel estimation expression under the Ricean fading model; Step 2. Considering the low-precision ADC and DAC quantization distortion, use the MRC receiver and UatF technology to obtain the closed expression of the UE rate; Step 3. Based on the MMSE channel estimation expression and the UE rate closed expression, establish a WSR optimization problem related to the power control coefficient, and convert the original optimization problem into a convex problem based on the quadratic transformation and Lagrange duality method. Design a low-complexity method that can solve the convex problem in a closed form and obtain the power coefficient; Step 4: Dynamically adjust the UE transmit power according to the power coefficient to achieve the WSR optimization goal. 2. A low-complexity WSR optimization method for non-ideal cellular massive MIMO systems according to claim 1, characterized in that step 1: considering low-precision ADC quantization distortion, establishing an uplink pilot training model to obtain Ricean fading The MMSE channel estimation expressions under the model include: After the CPU allocates pilot signals to UEs, it is assumed that all UEs transmit pilot signals to the AP at the same time; the pilot signal received by the mth AP, that is, AP _m , is quantized by a low-resolution ADC and can be modeled as Among them, m = 1, 2, ..., M, M is the total number of APs, k = 1, 2, ..., K, K is the total number of UEs, is a set composed of UE subscripts, τ is the pilot length, ρ _p is the pilot transmission power of UE _k , g _mk is the Ricean fading channel vector between AP _m and the k-th UE, that is, the Ricean fading channel vector between UE _k , /> represents the pilot signal, the superscript H represents the conjugate transpose,/> is the Gaussian white noise matrix, N is the number of antennas of the AP,/> It is a complex domain, α _m represents the distortion factor that measures the degree of ADC distortion, Y _{m, p} represents the pilot signal before quantization, /> is the quantized noise signal; based on And using MMSE estimation technology, the MMSE estimate of channel g _mk is: in, Represents the subscript set of all UEs using the same pilot as UE _k , j is the subscript of other UEs different from k, β _mk is the equivalent large-scale fading coefficient between AP _m and UE _k , β _mj is AP _m The equivalent large-scale fading coefficient between UE j and UE _j , σ ² is the noise power. 3. A low-complexity WSR optimization method for non-ideal cellular massive MIMO systems according to claim 2, characterized in that step 2: considering low-precision ADC and DAC quantization distortion, using MRC receiver and UatF technology to derive Obtain the closed expression expression of UE rate, including: Considering the quantization distortion of low-precision ADC and DAC, an uplink data transmission model is established based on the MRC receiver. The data signal received by the CPU can be modeled as Among them, λ _m represents the distortion factor that measures the degree of DAC distortion, is the DAC quantization noise at AP _m , ρ _u is the data transmission power of UE, η _j represents the power control coefficient of UE _j , q _j represents the data signal of UE _j , n _m is the Gaussian white noise at AP _m ,/> then represents the ADC quantization noise at AP _m ; using UatF technology for the above equation, the closed-form expression of the rate lower bound of UE _k can be derived as in In the above formula, K _mk and K _mj represent the Rice K factor, γ _mk and γ _mj represent the channel estimation quality factor, θ _mk represents the arrival incident angle between UE _k and AP _m , θ _mj is UE _j and AP _m angle of arrival. 4. A low-complexity WSR optimization method for non-ideal cellular massive MIMO systems according to claim 2, characterized in that step 3 is to establish based on the MMSE channel estimation expression and the UE rate closed expression. Regarding the WSR optimization problem of power control coefficient, based on the quadratic transformation and Lagrange duality method, the original optimization problem is converted into a convex problem, and a low-complexity method that can solve the convex problem in a closed form is designed to obtain the power coefficient, including: The optimization problem with maximizing WSR as the goal and using the power control coefficient of the UE as the independent variable is modeled as Among them, w _k represents the rate weight of UE _k , Indicates uplink WSR; Constraint 1 stipulates that the actual transmit power of the UE should be greater than or equal to 0 and cannot exceed its maximum transmit power; For optimization problems The Lagrangian dual transformation technique is used for it,/> Can be equivalently converted to Among them, ξ _k represents the newly added auxiliary variable; for f (η _k , ξ _k ), find the first-order partial derivative of ξ _k and let it be 0, we can get ξ _k as Will/> After substituting f(η _k , ξ _k ), the optimization problem/> Equivalent to for optimization problems Perform a second transformation, which is equivalent to Among them, δ _k represents the newly added auxiliary variable; find the first-order partial derivative of g(η _k , δ _k ) with respect to δ _k and set it to 0, we can get δ _k as in, Will/> Substitute optimization problem/> Solve/> Regarding the first-order partial derivative of eta _k and letting it be 0, the power control coefficient can be obtained as The optimal value of the power control coefficient can be obtained through several iterations 5. A low-complexity WSR optimization method for non-ideal cellular massive MIMO systems according to claim 4, characterized in that the optimal value of the power control coefficient can be obtained through several iterations The steps include: Step1: Initialize the number of iterations i←0, define the tolerance error ε>0, and set the initial feasible solution as Calculate the initial WSR, denoted as/> Step2: Update for/> Step3: Update for/> Step4: Update for/> Step5: Based on Update/> Step6: Judgment whether it is established; Step7: If it is not established, let i←i+1 and repeat steps Step2-Step5; Step8: If established, let then/> Optimization problem for original WSR/> A suboptimal solution of is the optimal value of the power control coefficient. 6. A low-complexity WSR optimization device for non-ideal cellular massive MIMO systems, characterized in that the device includes: Channel estimation module: used to consider low-precision ADC quantization distortion, establish an uplink pilot training model, and obtain the MMSE channel estimation expression under the Ricean fading model; UE rate module: used to consider low-precision ADC and DAC quantization distortion, and use MRC receiver and UatF technology to obtain the closed expression of UE rate; Solving module: used to establish a WSR optimization problem related to the power control coefficient based on the MMSE channel estimation expression and the UE rate closed expression, and convert the original optimization problem into a convex one based on the quadratic transformation and Lagrange duality method. problem, design a low-complexity method that can solve the convex problem in a closed form, and obtain the power coefficient; Adjustment module: used to dynamically adjust the UE transmit power according to the power coefficient to achieve WSR optimization goals. 7. A low-complexity WSR optimization device for non-ideal cellular massive MIMO systems, which is characterized by including a processor and a storage medium; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1 to 5. 8. A computer-readable storage medium having a computer program stored thereon, characterized in that when the program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.