Coordinated multi-point analog beam synthesis and ai-assisted accelerated computation method thereof

Abstract

The application discloses a kind of multi-point cooperation analog beam synthesis and its AI auxiliary acceleration calculation method, belong to the field of cellular MIMO wireless communication.This method uses the angle estimation capability of system to carry out user angle estimation and interference angle estimation, constructs the uplink communication beam synthesis problem model of multi-point cooperation cellular MIMO system;Based on ADMM framework, a two-stage solution method is proposed, which can independently solve analog beamforming at each distributed AP using deep expansion technique, after determining analog beamforming, digital beamforming solution can be performed at CPU to obtain the final beamforming;Based on optimal beamforming, the uplink communication beam synthesis problem model is simulated, and the performance indicators are calculated and recorded.The application improves the analog beam synthesis effect of multi-point cooperation cellular MIMO system through intelligent method based on model, and has the advantages of high real-time, low link overhead and low computational complexity.

Description

Technical Field

[0001] This invention relates to the field of cellular-free MIMO wireless communication technology, and in particular to a multi-point cooperative analog beamforming synthesis method and its AI-assisted accelerated computing method. Background Technology

[0002] In recent years, non-cellular systems have attracted much attention due to their advantages in transmission reliability and transmission rate. However, interference immunity remains a major challenge for non-cellular systems. Especially in the millimeter-wave band, the quality of signal propagation depends on the signal propagation conditions of the Loss of Sight (LoS) path; therefore, maintaining the signal quality of the LoS path ensures stronger interference immunity. However, an unavoidable problem is that in the presence of interference, it is impossible to accurately estimate the target angle and interference angle, which will affect the performance of subsequent signal processing algorithms. Therefore, in subsequent beamforming design, more targeted designs are needed to combat the problem of beam alignment errors caused by angle estimation errors.

[0003] Drawing inspiration from beam pattern synthesis in radar, wide main lobes and wide nulls can also be designed in the beamforming of millimeter-wave communication, thus making the beam more robust to angle errors. In practical multi-point cooperative non-cellular MIMO (Multiple-Input, Multiple-Output) systems, to distinguish distant targets from interference, access points (APs) can be arranged according to a certain pattern, forming a virtual large MIMO array. Furthermore, through a hybrid architecture millimeter-wave MIMO system, the beam can achieve performance while avoiding excessive fronthaul and hardware overhead. Therefore, considering the characteristics of practical non-cellular MIMO systems and the propagation characteristics of the millimeter-wave band, a targeted and efficient beamforming design is needed to meet the corresponding wide beam requirements while maintaining low computational complexity. Summary of the Invention

[0004] This invention provides a multi-point cooperative analog beamforming and its AI-assisted accelerated calculation method to achieve efficient and real-time anti-interference effect in multi-point cooperative non-cellular MIMO systems.

[0005] This invention provides a multi-point cooperative analog beamforming synthesis and its AI-assisted accelerated computation method, comprising the following steps:

[0006] S1. Utilize the angle estimation capability of the multi-point cooperative non-cellular MIMO system to perform user angle estimation and interference angle estimation, and construct an uplink communication beam synthesis problem model for the multi-point cooperative non-cellular MIMO system.

[0007] S2. Based on the ADMM (cross-direction multiplier method) framework, the uplink communication beamforming problem model is solved in two stages. First, the simulated beamforming is solved at each distributed AP using deep unfolding technology. After determining the simulated beamforming, the digital beamforming is solved at the CPU to obtain the optimal beamforming.

[0008] S3. Based on the optimal beamforming, the uplink communication beamforming problem model is simulated, and the performance indicators are calculated and recorded.

[0009] Optionally, in one embodiment of the present invention, step S1 specifically includes:

[0010] S11, based on the millimeter-wave LoS single-path communication link between the user, AP and jammer, receives user angle estimation information and jamming angle estimation information from the non-cellular uplink;

[0011] S12, construct the uplink communication beam synthesis problem model of the multi-point cooperative non-cellular MIMO system based on the received user angle estimation information and interference angle estimation information.

[0012] Optionally, in one embodiment of the present invention, step S2 specifically includes:

[0013] S21. Based on the characteristics of multi-point cooperative non-cellular MIMO systems, analog beamforming and digital beamforming optimization are separated to form a two-stage optimization problem. Analog beamforming is solved at each AP, and digital beamforming is solved at the CPU.

[0014] S22, each optimization subproblem is obtained at each AP, and solved using the ADMM framework. The deep unfolding technique is used to simulate beamforming at the AP.

[0015] S23. The analog beamforming is fed back to the CPU using the AP. A digital beamforming optimization subproblem is constructed at the CPU, and the digital beamforming is solved using the ADMM framework.

[0016] Optionally, in one embodiment of the present invention, step S3 specifically includes:

[0017] S31, Based on the optimal beamforming, determine the transmission parameters of the multi-point cooperative non-cellular MIMO system, including analog beamforming and digital beamforming at the receiver and minimum main lobe jitter.

[0018] S32, input the transmission parameters into the uplink communication beamforming problem model, and simulate and calculate the beam pattern and gain in the multi-point cooperative non-cellular MIMO system, including the beam pattern of a single AP and the beam pattern of multiple APs.

[0019] S33, draw charts based on performance indicators to perform performance analysis.

[0020] Optionally, in one embodiment of the present invention, in step S1, the uplink communication beamforming problem model aims to minimize the main lobe jitter of the beam and satisfies constraints on the main lobe width, null width and depth, side lobe level, and analog beamforming unit modulus. The uplink communication beamforming problem model is as follows:

[0021]

[0022] Among them, ε is the main lobe jitter, w BB For digital beamforming, To simulate beamforming, a(θ) m ) is the array guidance vector, θ m θ is the discrete angle parameter of the main lobe calculated based on user angle estimation information. n η is the discrete angle parameter of the null trap calculated based on the interference angle estimation information. Z θ represents the null depth limit. s η is the discrete angle parameter of the sidelobe. SL Indicates sidelobe level limitation. These are the discrete angle sets of the main lobe, null, and side lobes, respectively. for

[0023] The set of elements for simulating a beam.

[0024] Optionally, in one embodiment of the present invention, in step S22, with the goal of maximizing the simulated beam gain, an optimization sub-problem is established at each AP, specifically as follows:

[0025]

[0026] Where, ε l α is the beam gain, and α is the given main lobe jitter reference range.

[0027] Optionally, in one embodiment of the present invention, in step S22, the ADMM framework is used for solving, and depth unfolding technology is used to perform simulated beamforming at AP, including:

[0028] S221. Establish an optimization problem-solving framework based on ADMM. First, write out the augmented Lagrangian function of the optimization problem and determine the constraints.

[0029] S222: Solve the problem alternately for different variable groups. Stop the iteration when the target value converges or the maximum number of iterations is reached to obtain the optimal simulated beamforming.

[0030] S223 utilizes depth expansion technology to assist in solving the least squares subproblem of simulated beamforming, thereby obtaining the optimal step size of the Riemann gradient descent method and accelerating the convergence speed.

[0031] Optionally, in one embodiment of the present invention, in step S23, the form of the digital beamforming optimization subproblem constructed at the CPU is the same as the form of the original optimization problem, wherein the simulated beamforming is known.

[0032] The multi-point cooperative simulated beamforming and its AI-assisted accelerated computing method in this invention comprehensively considers the anti-interference transmission problem of non-cellular millimeter-wave MIMO systems in the presence of interference, increases the system's ability to resist interference, ensures low overhead requirements for computation while ensuring anti-interference transmission, and meets the needs of anti-interference transmission in complex interference environments.

[0033] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0034] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0035] Figure 1 A flowchart illustrating a multi-point cooperative analog beam synthesis and its AI-assisted accelerated computation method according to an embodiment of the present invention;

[0036] Figure 2 This is a flowchart of step S1 in an embodiment of the present invention;

[0037] Figure 3 This is a flowchart of step S2 in an embodiment of the present invention;

[0038] Figure 4 This is a flowchart of step S3 in an embodiment of the present invention;

[0039] Figure 5 This is a flowchart of the high-efficiency beamforming design algorithm for multi-point cooperative non-cellular MIMO based on depth unfolding, according to an embodiment of the present invention.

[0040] Figure 6 This is a schematic diagram of the architecture of the hyperparameter network according to an embodiment of the present invention;

[0041] Figure 7 This is a schematic diagram of the deep unfolding architecture according to an embodiment of the present invention;

[0042] Figure 8 This is a schematic diagram illustrating the step size determination process of the conventional Riemann gradient method in an embodiment of the present invention.

[0043] Figure 9 This is a beam gain diagram of a single AP analog beam according to an embodiment of the present invention;

[0044] Figure 10 This is a comparison diagram of the gain of single-AP analog beam and multi-AP hybrid beam in an embodiment of the present invention;

[0045] Figure 11 This is a comparison chart showing the time required for beamforming to be solved using the traditional Riemann gradient descent method and the depth unfolding technique, according to an embodiment of the present invention. Detailed Implementation

[0046] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0047] Figure 1 This is a flowchart illustrating a multi-point cooperative analog beamforming and its AI-assisted accelerated computation method according to an embodiment of the present invention.

[0048] like Figure 1 As shown, the multi-point cooperative simulated beam synthesis and its AI-assisted accelerated computation method include the following steps:

[0049] S1 utilizes the angle estimation capability of a multi-point cooperative non-cellular MIMO system to perform user angle estimation and interference angle estimation, and constructs an uplink communication beam synthesis problem model for a multi-point cooperative non-cellular MIMO system.

[0050] In step S1, the uplink communication beamforming problem model aims to minimize the main lobe jitter of the beam, while satisfying constraints on the main lobe width, null width and depth, side lobe level, and the unit modulus of the simulated beamforming. The uplink communication beamforming problem model is as follows:

[0051]

[0052] Among them, ε is the main lobe jitter, w BB For digital beamforming, To simulate beamforming, a(θ) m ) is the array guidance vector, θ m θ is the discrete angle parameter of the main lobe calculated based on user angle estimation information. n η is the discrete angle parameter of the null trap calculated based on the interference angle estimation information. Z θ represents the null depth limit. s η is the discrete angle parameter of the sidelobe. SL Indicates sidelobe level limitation. These are the discrete angle sets of the main lobe, null, and side lobes, respectively. This is the set of elements for simulating a beam. Based on the user's angle estimation information and the received interference angle estimation information, an angle interval is selected within a certain range to the left and right, and then discretely sampled to obtain the corresponding discrete angle parameters.

[0053] This invention proposes a multi-point cooperative simulated beamforming method and its AI-assisted accelerated calculation, establishing an optimization model. The goal is to minimize the main lobe jitter of the beam, while satisfying constraints on main lobe width, null width and depth, side lobe level, and the unit modulus of the simulated beamforming.

[0054] The goal of minimizing beam jitter is to ensure good signal quality in the direction of the main lobe of the received beam. By designing a reasonable beamforming scheme, the overall system's resistance to interference is optimized, thereby improving communication quality and reliability during uplink transmission. By adjusting the null width and depth, the system can eliminate the impact of interference signals on the communication system, enabling it to tolerate a certain level of interference power.

[0055] In embodiments of the present invention, such as Figure 2 As shown, step S1 specifically includes:

[0056] S11, based on the millimeter-wave LoS single-path communication link between the user, AP and jammer, receives user angle estimation information from the non-cellular uplink and receives jamming angle estimation information. Both angle information have certain estimation errors.

[0057] S12, construct an uplink communication beamforming problem model for a multi-point cooperative non-cellular MIMO system based on the received user angle estimation information and interference angle estimation information, including optimization of analog beamforming and digital beamforming.

[0058] A cellless millimeter-wave MIMO system consists of several multi-antenna access points (APs) and several multi-antenna users, with a finite number of multi-antenna jammers interfering with the communication. Each AP is connected to a central processing unit (CPU) via a fronthaul link to coordinate transmission for the users, and all APs share system information. During uplink transmission, each user communicates on different frequency bands, thus eliminating inter-user interference. However, due to the presence of external jammers, each user receives interference signals from the jammers, further affecting normal communication quality.

[0059] In an embodiment of the present invention, step S12 specifically includes:

[0060] S12a is for antenna array guidance vector modeling in non-cellular millimeter-wave LoS scenarios, and prepares relevant model data for the angles of users and jammers.

[0061] S12b, based on the array guidance model, uses beam synthesis theory to construct a beamforming optimization problem of a related form, which involves the fusion of analog beamforming and digital beamforming.

[0062] By establishing a problem model based on cellular-free millimeter-wave hybrid beamforming, we can effectively obtain the real physical model corresponding to the problem architecture, which helps to design anti-interference communication strategies for multi-point collaborative cellular-free MIMO systems.

[0063] S2, based on the ADMM framework, solves the uplink communication beamforming problem model in two stages. First, the simulated beamforming is solved at each distributed AP using deep unfolding technology. After determining the simulated beamforming, the digital beamforming is solved at the CPU to obtain the optimal beamforming.

[0064] In embodiments of the present invention, such as Figure 3 As shown, step S2 specifically includes:

[0065] S21. Based on the characteristics of a non-cellular multi-point cooperative system, analog beamforming and digital beamforming optimization are separated to form a two-stage optimization problem. Analog beamforming is solved at each AP, and digital beamforming is solved at the CPU.

[0066] S22, each AP has its own optimization subproblem, which is solved using the ADMM framework. In order to reduce the computational complexity of the simulated beamforming solution, the deep unfolding technique is introduced, and the deep unfolding technique is used to perform simulated beamforming solution at the AP.

[0067] S23. The analog beamforming is fed back to the CPU using the AP. A digital beamforming optimization subproblem is constructed at the CPU, and the digital beamforming is solved using the ADMM framework.

[0068] In step S21, based on the form of the uplink communication beam synthesis problem model for a multi-point cooperative cellular-free MIMO system, the problem is decomposed into a two-stage solution. The first stage is to solve for the analog beam at the AP, and the second stage is to solve for the digital beam at the CPU. In the first stage, considering the unity modulus constraint of the analog beam, the subproblem at the AP becomes maximizing the gain of the analog beam. In the second stage, the subproblem at the CPU remains in its original form, but the analog beam is now known.

[0069] To better utilize the distributed collaboration capabilities of multiple APs while reducing fronthaul link overhead, an optimization sub-problem is established at each AP by decomposing the analog and digital beamforming optimization problems and aiming to maximize beam gain:

[0070]

[0071] Where, ε l α is the beam gain, and α is the given main lobe jitter reference range.

[0072] Considering the unity modulus constraint of the simulated beam, the subproblem objective at each AP is to maximize the beam gain. Here, α is the given main lobe jitter reference range, and ε... l For beam gain. After obtaining the analog beam at each AP, the AP sends the analog beam to the CPU via the fronthaul link. The CPU then solves for the digital beam, with the problem in the form of:

[0073]

[0074] Among them, W RF This is the simulated beamforming obtained from the solution. This two-stage solution fully utilizes the distributed processing capabilities of the non-cellular system, effectively reducing the computational overhead at the CPU level.

[0075] In step S22, the ADMM framework is used for solving the problem, and depth unfolding technology is used to simulate beamforming at AP, including:

[0076] S221. Establish an optimization problem-solving framework based on ADMM. First, write out the augmented Lagrangian function of the optimization problem and determine the constraints.

[0077] S222: Solve the problem alternately for different variable groups. Stop the iteration when the target value converges or the maximum number of iterations is reached to obtain the optimal simulated beamforming.

[0078] S223 utilizes depth expansion technology to assist in solving the least squares subproblem of simulated beamforming, thereby obtaining the optimal step size of the Riemann gradient descent method and accelerating the convergence speed.

[0079] S3 is based on the optimal beamforming and uplink communication beam synthesis problem model to perform simulation, calculate and record performance indicators.

[0080] In embodiments of the present invention, such as Figure 4 As shown, step S3 specifically includes:

[0081] S31, Based on optimal beamforming, determine the transmission parameters of the multi-point cooperative non-cellular MIMO system, including analog beamforming and digital beamforming at the receiver and minimum main lobe jitter.

[0082] S32, input the transmission parameters into the uplink communication beam synthesis problem model, and simulate and calculate the beam pattern and gain in a multi-point cooperative non-cellular MIMO system, including the beam pattern of a single AP and the beam pattern of multiple APs;

[0083] S33. Based on performance indicators, charts are drawn and computational complexity is analyzed to perform performance analysis on a multi-point cooperative analog beamforming and its AI-assisted accelerated computation method.

[0084] In a further embodiment, based on the known system deployment, and considering the main lobe width, null width and depth, side lobe level, and analog beamforming unit modulus constraints, an uplink communication beamforming problem model for a multi-point cooperative non-cellular MIMO system is constructed. Based on the distributed processing characteristics of non-cellular systems and the features of analog and digital beams, the uplink communication beamforming problem is decomposed into a two-stage solution: the analog beam is solved at the AP (Access Point) with the incorporation of depth unwrapping technology, and the digital beam is solved at the CPU, ultimately yielding the optimal solution to the original problem. Simulation results show that the proposed method can reasonably design system beamforming with relatively low computational complexity, achieving reliable anti-interference performance.

[0085] In the embodiments of the present invention, during the modeling of multi-point cooperative simulated beamforming problem, a model of a single array guidance vector is constructed, and considering the characteristics of non-cellular systems, a multi-array array guidance vector model is constructed. Problem modeling is performed based on the structure of hybrid beamforming. At the same time, according to the requirements and with reference to beamforming theory, constraints with wide main lobe, wide null, and low sidelobe level are constructed to minimize main lobe level jitter.

[0086] In embodiments of the present invention, the performance analysis of multi-point cooperative simulated beam synthesis and its AI-assisted accelerated computation method includes:

[0087] Analyze the beam pattern and gain of a single AP beamforming.

[0088] Analyze the beam pattern and gain of multiple AP hybrid beamforming.

[0089] This study analyzes the computational complexity and real-time performance of deep unfolding techniques compared to traditional methods.

[0090] In embodiments of the present invention, the multi-point cooperative simulated beam synthesis and its AI-assisted accelerated computation method include:

[0091] Define the variables and parameters in a multi-point cooperative non-cellular MIMO system, where the variables include analog and digital beamforming and main lobe jitter level, and the parameters include main lobe width, null width and depth, side lobe level and the number and shape of antennas per AP array;

[0092] Based on the variables and parameters, as well as the performance indicators of the multi-point cooperative non-cellular MIMO system, the objective function of beamforming in the multi-point cooperative non-cellular MIMO system is constructed to minimize the main lobe level jitter. The constraints of the multi-point cooperative non-cellular MIMO system are defined, including the upper and lower limits of the main lobe jitter, null depth and width limits, side lobe level constraints, and constant mode constraints of simulated beamforming.

[0093] To address the problem of constructing a multi-point cooperative simulated beam synthesis and its AI-assisted accelerated computation method for the objective function and constraints, a non-convex optimization problem is obtained.

[0094] A model for uplink communication beamforming in a multi-point cooperative non-cellular MIMO system is constructed. Based on analog and digital beamforming, the problem is decomposed into a two-stage optimization problem. Using the ADMM framework, the non-convex optimization problem is further decomposed into sub-problems involving analog and digital beamforming at the receiver. The augmented Lagrange multiplier method combined with the penalty function method is used to solve the two sub-problems separately. Deep unfolding technology is applied when solving the analog beam to obtain the optimal solution for the analog beam and accelerate convergence. The analog beam is then fed back to the CPU to solve the digital beam, thus obtaining all optimal solutions to the non-convex optimization problem.

[0095] In embodiments of the present invention, the simulation parameter settings for the multi-point cooperative simulated beam synthesis and its AI-assisted accelerated computation method include:

[0096] Based on the performance indicators of the multi-point cooperative non-cellular MIMO system, the simulation parameters of the multi-point cooperative non-cellular MIMO system are set, including the number, location, and effective area of ​​APs, users, and jammers.

[0097] Based on the beamforming algorithm of the multi-point cooperative non-cellular MIMO system, the optimization parameters of the multi-point cooperative non-cellular MIMO system are set, including the objective function, constraints, basic framework of the optimization algorithm, convergence conditions, etc.

[0098] Based on the application scenarios of beamforming algorithms for multi-point cooperative non-cellular MIMO systems, the application parameters for transmission in multi-point cooperative non-cellular MIMO systems are set, including application type, etc.

[0099] In embodiments of the present invention, the simulation results analysis of multi-point cooperative simulated beam synthesis and its AI-assisted accelerated computation method includes:

[0100] Run the simulation algorithm for anti-interference beamforming design of multi-point cooperative non-cellular MIMO system, and record the simulation results of beamforming of multi-point cooperative non-cellular MIMO system, including beam pattern and gain.

[0101] Furthermore, the specific algorithm flow for the high-efficiency beamforming design method for multi-point cooperative cellular-free MIMO based on depth unfolding is as follows: Figure 5 As shown, specifically:

[0102] Step a: Decompose the original optimization problem into analog beam design on the AP side and digital beam design on the CPU side;

[0103] Step b: For the simulated beam design on the AP side, solve the sub-problem:

[0104]

[0105] According to the ADMM framework, the problem is first transformed into:

[0106]

[0107] in, Write down the augmented Lagrange function:

[0108]

[0109] Solve subproblem 1 alternately:

[0110]

[0111] Sum Problem 2:

[0112]

[0113] Then update the penalty parameters:

[0114]

[0115] The three problems are solved alternately until the target value converges. In solving subproblem 1, a classic method is Riemann gradient descent. To accelerate the algorithm using AI deep decomposition technology, a hyperparameter network is used to optimize the iteration step size, thereby accelerating the convergence speed of the traditional method and reducing the corresponding computational complexity, achieving high efficiency and real-time performance. For the AI-assisted part, a basic schematic diagram of the deep decomposition hyperparameter neural network structure in this algorithm is given, as follows: Figure 6 As shown in the diagram, the entire network consists of five complex linear layers. The output activation function of the first to fourth layers is the CReLU function. The output of the last layer is the sum of the real and imaginary parts, and to ensure the non-negativity of the output step size parameter, the activation function of the last layer is set to the absolute value Abs function. The input of the entire network is the least squares solution to the problem, the corresponding Euclidean gradient, and the corresponding Riemann gradient. The output is the step size parameter for all iterations. The expression for CReLU is:

[0116] CReLU(z)=max{0,real(z)}+1i*max{0,imag(z)}

[0117] Figure 7 and Figure 8 The diagrams show the flowcharts for both AI-assisted Riemann gradient descent and the traditional Riemann gradient descent method. The difference lies in the fact that the hyperparameter network of the AI-assisted deep unfolding method can predict the step size parameters for all iterations at once, while the traditional step size determination method based on the Armijo backtracking search requires a large amount of computation and judgment in each iteration, and the computational complexity of this step is difficult to measure accurately, making it unacceptable in practical applications. The AI-assisted deep unfolding method, however, allows for CPU-parallel acceleration of neural network computation in actual hardware computing, thus offering a significant advantage in reducing the algorithm's computational complexity.

[0118] Step c: For the digital beamforming design on the CPU side, solve the subproblems:

[0119]

[0120] Based on the ADMM framework, the problem is transformed into:

[0121]

[0122] in, a cpu (θ i ) = W RF,H a(θ i Write down the augmented Lagrange function:

[0123]

[0124] Solve subproblem 3 alternately:

[0125]

[0126] Sum Problem 4:

[0127]

[0128] st

[0129]

[0130] Then update the penalty parameters:

[0131]

[0132] Continue until the target value converges. At this point, all the variables to be optimized have been obtained.

[0133] To verify the anti-interference transmission performance of the present invention, the following simulation experiments were conducted:

[0134] Five users, ten access points (APs), and two jammers are configured within a large cubic area. The APs are evenly distributed along one edge of the cube's base, with fixed and relatively close distances between them. The users and jammers are also evenly distributed in space, with the user positions concentrated but relatively far from the APs, ensuring that all AP arrays meet far-field conditions. Each AP is equipped with a single-radio-chain phased array with 64 antennas, while both users and jammers are equipped with a single antenna.

[0135] First, the single-AP simulated beamforming algorithm was simulated under single interference angle and single target angle conditions. The simulation results are as follows: Figure 9 As shown in the figure, the horizontal axis represents the angle range, and the vertical axis represents the beam gain. It can be seen that the proposed beamforming design algorithm can achieve the requirements of a wide main lobe and a wide null, while ensuring the level of the sidelobes.

[0136] Then, the gain comparison charts for single-AP simulated beam and multi-AP beam were compared, and the simulation results are as follows. Figure 10 As shown in the figure, the horizontal axis represents the angle range, and the vertical axis represents the beam gain. It can be seen that, under the same interference and target, the hybrid beamforming effect of multiple APs is significantly better than the simulated beamforming effect of a single AP. Specifically, after multi-point cooperation, the jitter of the main lobe is significantly reduced.

[0137] To demonstrate the algorithm's low complexity, this invention presents the number of iterations and running time required for each solution by the algorithm incorporating depth expansion and the traditional algorithm, as follows: Figure 11 As shown, the depth unfolding method requires significantly fewer iterations than the Riemann gradient descent method, effectively reducing computational complexity.

[0138] This invention proposes a multi-point cooperative simulated beamforming and its AI-assisted accelerated computation method. It utilizes the system's angle estimation capabilities to estimate user and interference angles, constructing an uplink communication beamforming problem model for a multi-point cooperative non-cellular MIMO system. Based on the ADMM framework, a two-stage solution method is proposed, allowing independent solution of simulated beamforming at each distributed AP using deep unfolding technology. After determining the simulated beamforming, digital beamforming is solved at the CPU to obtain the final beamforming. The uplink communication beamforming problem model is simulated based on optimal beamforming, and performance indicators are calculated and recorded. This invention improves the simulated beamforming effect of a multi-point cooperative non-cellular MIMO system through a model-based intelligent method, and has advantages such as high real-time performance, low link overhead, and low computational complexity.

[0139] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0140] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0141] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

Claims

1. A method for coordinated multi-point analog beam synthesis and its AI-assisted accelerated computation, characterized in that, Includes the following steps: S1. Utilize the angle estimation capability of the multi-point cooperative non-cellular MIMO system to perform user angle estimation and interference angle estimation, and construct an uplink communication beam synthesis problem model for the multi-point cooperative non-cellular MIMO system. S2, based on the ADMM framework, the uplink communication beamforming problem model is solved in two stages. First, the simulated beamforming is solved at each distributed AP using deep unfolding technology. After determining the simulated beamforming, digital beamforming is solved at the CPU to obtain the optimal beamforming. Step S2 specifically includes: S21. Based on the characteristics of multi-point cooperative non-cellular MIMO systems, analog beamforming and digital beamforming optimization are separated to form a two-stage optimization problem. Analog beamforming is solved at each AP, and digital beamforming is solved at the CPU. S22, each optimization subproblem is obtained at each AP, and solved using the ADMM framework. The deep unfolding technique is used to simulate beamforming at the AP. S23, use AP to transmit the simulated beamforming back to the CPU, construct the digital beamforming optimization subproblem at the CPU, and use the ADMM framework to solve the digital beamforming. S3, Simulate the uplink communication beamforming problem model based on the optimal beamforming, calculate and record the performance indicators; In step S1, the uplink communication beamforming problem model aims to minimize the main lobe jitter of the beam, while satisfying constraints on the main lobe width, null width and depth, side lobe level, and the unit modulus of the simulated beamforming. The uplink communication beamforming problem model is as follows: in, Main lobe jitter For digital beamforming, To simulate beamforming, For array guidance vectors, These are the discrete angle parameters of the main lobe calculated based on user angle estimation information. These are the discrete angle parameters of the null trap calculated based on the interference angle estimation information. Indicates the zero-deepness limit. These are the discrete angle parameters of the side lobes. Indicates sidelobe level limitation. , , These are the discrete angle sets of the main lobe, null, and side lobes, respectively. To simulate the beam's element set, based on the estimated angle, a certain angle interval is selected within a certain range to the left and right of the estimated angle, and then discretely sampled to obtain the corresponding discrete angle parameters. In step S22, with the goal of maximizing the simulated beam gain, an optimization sub-problem is established at each AP, specifically as follows: in, For beam gain, For the given main lobe jitter reference range; In step S22, the ADMM framework is used for solving the problem, and depth unfolding technology is used to simulate beamforming at AP, including: S221. Establish an optimization problem-solving framework based on ADMM. First, write out the augmented Lagrangian function of the optimization problem and determine the constraints. S222: Solve the problem alternately for different variable groups. Stop the iteration when the target value converges or the maximum number of iterations is reached to obtain the optimal simulated beamforming. S223 utilizes depth expansion technology to assist in solving the least squares subproblem of simulated beamforming, thereby obtaining the optimal step size of the Riemann gradient descent method and accelerating the convergence speed. In step S23, the digital beamforming optimization subproblem is constructed at the CPU in the same form as the original optimization problem, wherein the simulated beamforming is known.

2. The method according to claim 1, characterized in that, Step S1 specifically includes: S11, based on the millimeter-wave LoS single-path communication link between the user, AP and jammer, receives user angle estimation information and jamming angle estimation information from the non-cellular uplink; S12, construct the uplink communication beam synthesis problem model of the multi-point cooperative non-cellular MIMO system based on the received user angle estimation information and interference angle estimation information.

3. The method according to claim 1, characterized in that, Step S3 specifically includes: S31, Based on the optimal beamforming, determine the transmission parameters of the multi-point cooperative non-cellular MIMO system, including analog beamforming and digital beamforming at the receiver and minimum main lobe jitter. S32, input the transmission parameters into the uplink communication beamforming problem model, and simulate and calculate the beam pattern and gain in the multi-point cooperative non-cellular MIMO system, including the beam pattern of a single AP and the beam pattern of multiple APs. S33, draw charts based on performance indicators to perform performance analysis.

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