Joint optimization method and system for user-centric cell-free mimo system

Abstract

The application provides a user-centered joint optimization method and system of a cell-free MIMO system, and relates to the technical field of wireless communication. The method comprises the following steps: constructing a downlink of a UCCF millimeter wave mMIMO communication system, the mMIMO communication system comprising a plurality of randomly distributed access points AP and user equipment UE, each AP being equipped with a polarized reconfigurable array, and constructing a signal model; based on the signal model, constructing a corresponding channel model; and constructing an initial hybrid beamforming model; based on the channel model, optimizing the channel model with an optimal sum rate as a target; according to an optimal perturbation genetic algorithm, iteratively optimizing an AP-UE clustering matrix A, a polarization mask matrix B of all APs and the hybrid beamforming model to obtain a target joint optimization model. The application solves the problem that the performance of an existing cell-free large-scale MIMO system is limited in a dense user or dynamic network environment.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and specifically to a joint optimization method and system for a user-centric non-cellular MIMO system. Background Technology

[0002] In recent years, with the exponential growth in connection density and Quality of Service (QoS) requirements, technologies such as millimeter wave (mmWave) and massive MIMO (mMIMO) have been proposed to meet the needs of next-generation mobile communication networks. Furthermore, the high distance-based path loss caused by high-frequency transmission can be compensated for by directional beamforming with large antenna arrays, making millimeter wave and mMIMO mutually important. However, the propagation characteristics of millimeter wave signals, including high path loss and susceptibility to obstruction, pose significant challenges to traditional cellular network architectures.

[0003] To address these issues, cell-free massive MIMO architecture emerged, effectively eliminating cell boundaries in traditional cellular networks through the cooperation of distributed access points (APs), significantly improving coverage and spectrum efficiency. However, this architecture faces several challenges in practical deployment, including insufficient personalized services for users, poor interference management between users, inefficient resource allocation, and limited support for high-mobility scenarios. These shortcomings limit the system's performance in high-density user or dynamic network environments. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a user-centric joint optimization method and system for non-cellular MIMO systems, aiming to solve at least one technical problem existing in the background technology.

[0005] A first aspect of the present invention provides a joint optimization method for a user-centric cellular-free MIMO system, the method comprising: The downlink of the UCCF millimeter-wave mMIMO communication system is constructed. The mMIMO communication system includes multiple randomly distributed access points (APs) and user equipment (UEs). Each AP is equipped with a polarization reconfigurable array. A signal model is constructed based on the polarization reconfigurable array. Based on the signal model, a corresponding channel model is constructed; A greedy optimization algorithm with random annealing is used to obtain the AP-UE clustering matrix A, and an initial hybrid beamforming model is constructed. Based on the aforementioned channel model, the channel model is optimized with the polarization distribution of the polarization reconfigurable array, various beamforming parameters, and the AP-UE clustering matrix A as constraints, and with optimal sum and rate as the objective. Based on the optimal perturbation genetic algorithm, the AP-UE clustering matrix A, the polarization mask matrix B of all APs, and the hybrid beamforming model are iteratively jointly optimized to obtain the target joint optimization model.

[0006] Furthermore, the aforementioned joint optimization method for user-centric non-cellular MIMO systems includes the following steps: constructing the downlink of a UCCF millimeter-wave mMIMO communication system, where the mMIMO communication system comprises multiple randomly distributed access points (APs) and user equipment (UEs), each AP equipped with a polarization reconfigurable array; and constructing a signal model based on the polarization reconfigurable array. Specifically, these steps include: Each AP employs a single-port polarized reconfigurable antenna element, and the vertical RF array group of the polarized reconfigurable antenna element... and horizontal radio frequency array group The hybrid polarization section forms a polarization reconfigurable array, and the polarization state of the unit is switched by a switch / parasitic element circuit to achieve dynamic adjustment of the polarization distribution; Each polarization reconfigurable antenna element controls the shutdown and power distribution of each polarization stub through a circuit structure with an RF switching circuit, thereby enabling the polarization selection of the polarization reconfigurable antenna element in the two orthogonal directions V and H and in any projection direction. Each polarization reconfigurable antenna element supports individual configuration of the polarization rotation angle, which is pre-fixed by the polarization beamwidth of the polarization reconfigurable array in the X and Y directions.

[0007] Furthermore, in the aforementioned joint optimization method for user-centric cellular-free MIMO systems, the construction formula for the signal model based on the polarization reconfigurable array is as follows: Transmitted signal vector Represented as: ; in, Is sent to the Data symbols of a UE, and satisfying E[| | 2 ] =1, It is the first The AP corresponds to the first Digital baseband precoder for each UE express A matrix space with 1 row and 1 column, It is the first Analog RF beamformer for an AP This refers to the number of antennas at the transmitting end. for and The sum of the number of radio frequency chains, It is a vertical radio frequency array group. It is a horizontal radio frequency array group; ; in, Indicates the first One user; , Total number of users Maximum service per AP One UE, The polarization distribution of the user's polarization reconfigurable array V(H) is given, where the polarization reconfigurable array V(H) includes a polarization reconfigurable array with polarization dimension V and a polarization reconfigurable array with polarization dimension H. The received signal of the kth UE This is represented as the superposition of all signals sent from the AP to the UE after propagation through the channel, plus additive white Gaussian noise. : ; in, For users The received signal, To serve the first The AP set of each UE m For the first A subset of UEs served by an AP. Total number of users This represents the total number of APs. Indicates the first AP Polar subarray and Millimeter-wave polarization channels between polarized sub-users For other users The original data symbol vector, For other users The corresponding baseband beamforming matrix, It has a mean of zero and a covariance of A circularly symmetric Gaussian distributed additive noise vector.

[0008] Furthermore, the aforementioned joint optimization method for user-centric cellular-free MIMO systems, specifically includes the following steps for optimizing the channel model based on the channel model, using the polarization distribution of the polarization reconfigurable array, various beamforming parameters, and the AP-UE clustering matrix A as constraints, with optimal sum and rate as the objective: Calculate the ratio of the user's received signal to the interference plus noise (INF) ratio. Based on this INF ratio, the summation rate is: ; in, For users The ratio of received information to interference plus noise. For the sum and rate, Total number of users; Using the polarization distribution of the polarimetric reconfigurable array, various beamforming parameters, and the AP-UE clustering matrix A as constraints, and with optimal sum and rate as the objective, the channel model is optimized as follows: P: ; st ; ; ; ; in, ( ) represents the number of antennas in the V(H) polarimetric subarray. This represents the number of V / H polarized users, and B represents the polarization mask matrix, i.e., the polarization distribution of the mask matrix. and Let the constant modes and connectivity constraints of the V-polarized and H-polarized subarrays at the transmitter end be represented respectively. Each V-polarized antenna of the AP is connected to the first The V-RF chains are connected by a phase shifter, at which point the amplitude is constant. , This means that each user is served by at least one AP. This means that the number of users served by each AP cannot exceed the maximum number of UEs that can be accessed by the current AP.

[0009] Furthermore, the aforementioned joint optimization method for user-centric cellular MIMO systems, specifically includes the steps of obtaining the AP-UE clustering matrix A using a greedy optimization algorithm with stochastic annealing and constructing an initial hybrid beamforming model, which include: By calculating the channel gain of each AP-UE pair, the AP with the best channel conditions is allocated to the UE first. If the AP is already full, the AP with the second best channel gain is then allocated. After sorting the channel gains in descending order, connections are established sequentially, while ensuring that the number of UEs in each AP does not exceed the capacity limit. The final output is the AP-UE cluster matrix A.

[0010] Furthermore, in the aforementioned joint optimization method for user-centric cellular-free MIMO systems, the step of constructing an initial hybrid beamforming model specifically includes: Building a simulated beamformer and digital beamformer The initial hybrid beamforming model is composed of; Simulated beamformer It is used to align with the channel phase and incorporates the polarization mask matrix B to dynamically adjust the polarization distribution; Simulated beamformer Constructed as a block diagonal matrix: ; For each AP, initially By assigning two RF chains to each service user, one for V polarization and one for H polarization; Adding a polarization mask matrix to hybrid beamforming The selection effect, then the first The V-polarized and H-polarized RF chain column vectors for each user are designed as follows: ; ; in, For the first The user and the first Channel phase between APs and V and H are polarization mask vectors, respectively. and The m-th element, , These represent the number of V-polarized and H-polarized antennas, respectively. Digital beamformer The design adopts the global MMSE criterion, and first constructs the equivalent channel. : ; The global MMSE digital precoder is: ; in, As the normalization factor, for The conjugate transpose of the conjugate will Classified by AP It is used for baseband processing in each AP.

[0011] Furthermore, the aforementioned joint optimization method for user-centric cellular-free MIMO systems includes the following steps: Specifically, the step of iteratively jointly optimizing the AP-UE clustering matrix A, the polarization mask matrix B of all APs, and the hybrid beamforming model using the optimal perturbation genetic algorithm to obtain the target joint optimization model includes: Let Q and E represent the population size and the number of iterations, respectively. Each chromosome represents a polarization mask matrix B and a clustering matrix A. The chromosome encoding is in a concatenated form. ; The formula for calculating the population is as follows: ; in, Indicates the first The population in the next iteration, yes The Middle The 4th chromosome, i.e., the 1st chromosome A polarization mask matrix, assuming For the first The chromosome with the highest fitness in the next iteration is the polarization mask matrix B and the clustering matrix A with the highest fitness. The optimal clustering matrix A, polarization mask matrix B, and hybrid beamforming model are generated iteratively to obtain the joint optimization model of the target.

[0012] Another object of the present invention is to provide a joint optimization system for a user-centric cellular-free MIMO system, the system comprising: Equipped with modules for building the downlink of the UCCF millimeter-wave mMIMO communication system, the mMIMO communication system includes multiple randomly distributed access points (APs) and user equipment (UEs), each AP is equipped with a polarization reconfigurable array, and a signal model is built based on the polarization reconfigurable array; A construction module is used to construct a corresponding channel model based on the signal model; The clustering module is used to obtain the AP-UE clustering matrix A using a greedy optimization algorithm with random annealing, and to construct the initial hybrid beamforming model; An optimization module is used to optimize the channel model based on the channel model, with the polarization distribution of the polarization reconfigurable array, various parameters of beamforming, and AP-UE clustering matrix A as constraints, and with optimal sum and rate as the objective. The iterative module is used to iteratively and jointly optimize the AP-UE clustering matrix A, the polarization mask matrix B of all APs, and the hybrid beamforming model according to the optimal perturbation genetic algorithm, so as to obtain the target joint optimization model.

[0013] A third aspect of the present invention is to provide a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described steps of joint optimization of dynamic polarization subarray and AP clustering based on millimeter-wave Cell-Free MU-mMIMO system.

[0014] A fourth aspect of the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described method for joint optimization of dynamic polarization subarray and AP clustering based on a millimeter-wave Cell-Free MU-mMIMO system.

[0015] This invention constructs a UCCF (User-Centric Cell-Free) millimeter-wave mMIMO downlink system with multiple randomly distributed APs and UEs. Each AP is equipped with a polarization reconfigurable array, and a signal model and a channel model with a fused polarization matrix are established. A greedy optimization algorithm based on channel quality is used to obtain the AP-UE clustering matrix A, and an initial hybrid beamforming model is constructed. With polarization distribution, beamforming parameters, and the clustering matrix as constraints, the optimal perturbation genetic algorithm (OPGA) is used to jointly optimize the AP-UE clustering matrix A, the polarization mask matrix B of all APs, and the hybrid beamforming model, maximizing system performance and rate. This avoids the problems of insufficient personalized user services, poor inter-user interference management, low resource allocation efficiency, and poor adaptability to high-density / dynamic scenarios in traditional Cell-Free architectures. It also compensates for the performance bottlenecks caused by high path loss, susceptibility to obstruction, and lack of joint optimization of polarization and clustering in millimeter-wave systems. Ultimately, it achieves improved system spectrum utilization efficiency, effective suppression of multi-user interference, and coordinated optimization of load balancing and personalized services. It solves the problem of limited performance of existing cellless large-scale MIMO systems in dense user or dynamic network environments.

[0016] In addition, the present invention has at least the following beneficial effects: (1) A suboptimal AP clustering algorithm is dynamically assigned to each user, which ensures that each user is served by at least one AP and limits the number of users served by each AP to no more than the actual load capacity of the AP. While ensuring that each user has a high probability of obtaining the optimal AP clustering, this algorithm also incorporates a certain perturbation strategy to prevent its dynamic clustering algorithm from getting trapped in local optimization too early. Thus, it achieves load balancing while improving system throughput, and enhances the scalability and practicality of the system.

[0017] (2) For the first time, dynamic AP-UE clustering and polarization-reconfigurable antenna technology are combined to construct a joint optimization framework for interference suppression. This paper innovatively incorporates user-side dynamic clustering and transmitter-side polarization optimization into a unified design. AP clustering is used to avoid long-range interference in the spatial domain, while polarization reconfiguration is used to eliminate co-polarization interference in the polarization domain, significantly improving the robustness and scalability of the Cell Free system. This significantly enhances the performance boundary of cell-free systems in dense user scenarios.

[0018] (3) This invention establishes an efficient OPGA-based joint optimization method to address the high-dimensional optimization challenges arising from the strong coupling between AP-UE clustering, hybrid beamforming, and beam polarization selection optimization problems. Specifically, we designed a chromosome to simultaneously encode the AP-UE clustering matrix A and the polarization mask matrix B of all APs, enabling the evolutionary algorithm to jointly search the coupled space-polarization domain. This method implements a unified optimization framework that integrates dynamic AP-UE clustering and distributed polarization beamforming within AP clusters. Attached Figure Description

[0019] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a flowchart illustrating the joint optimization method for a user-centric non-cellular MIMO system according to the first embodiment of the present invention. Figure 2 This is a block diagram of a cellular-free MIMO system. Figure 3 This is a block diagram of the dynamic polarization subarray structure in the first embodiment of the present invention; Figure 4 This is a schematic diagram of an orthogonal dual-polarization reconfigurable antenna unit circuit. Figure 5 Schematic diagram of the rotation angle configuration for the mMIMO UPA surface antenna array; Figure 6 This is a polarization distribution diagram of the polarization mask matrix; Figure 7 To compare the system performance test charts with and without the AP-UE clustering matrix A; Figure 8 Test plots of sum rate versus signal-to-noise ratio for hybrid beamforming models with different optimization algorithms; Figure 9 Test plots showing the sum rate versus iteration number for hybrid beamforming models with different optimization algorithms. Detailed Implementation

[0020] To make the objectives, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Several embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of the present invention will be more thorough and complete.

[0021] Example 1 Please see Figure 1The figure shows a joint optimization method for a user-centric cellular MIMO system provided in the first embodiment of the present invention, the method including steps S10-S14.

[0022] Step S10: Construct the downlink of the UCCF millimeter-wave mMIMO communication system. The mMIMO communication system includes multiple randomly distributed access points (APs) and user equipment (UEs). Each AP is equipped with a polarization reconfigurable array. Construct a signal model based on the polarization reconfigurable array. like Figure 2 As shown, assuming a cellless massive MIMO system with downlink users, randomly distributed... AP and Each UE (User Equipment) meets the following requirements: Each AP is equipped with a uniform planar array consisting of Nt polarization-reconfigurable antenna elements, which together serve the network. A single-antenna user, and through The radio frequency chain is connected to the baseband processing unit. and The sum of the number of radio frequency chains is The number of radio links on each AP is consistent with the number of users, i.e. =K.

[0023] Specifically, such as Figure 3 As shown, a single-port polarized reconfigurable antenna is used to construct the transmit mMIMO antenna array. This antenna avoids the increased RF (baseband) processing, power consumption, and feeding complexity caused by dual-polarized antennas. The antenna polarization can be changed using a polarization selection circuit, that is, by adjusting the polarization state of the single-port polarized reconfigurable antenna using switches and parasitic elements, causing the polarization reconfigurable array V(H) to generate different subarray structures, vertically oriented RF array groups... and horizontal radio frequency array group Maintaining hybrid connectivity, vertical RF array group and horizontal radio frequency array group All RF chains are connected to all elements of the polarization-reconfigurable array V(H). By combining the design concepts of hybrid and dynamic subarray structures with the polarization-reconfigurable array, the performance degradation caused by fixed subarrays is mitigated.

[0024] By incorporating switching components and parasitic elements into the polarization reconfigurable array, the connection between the polarization reconfigurable array and the antenna array can be dynamically adjusted, thereby optimizing the antenna array layout. Figure 4As shown, each antenna element controls the shutdown and power distribution of each polarization stub through a circuit design with RF switching circuits such as PIN switches, multi-polarization stub antenna structure, and power divider, thereby realizing polarization selection of the polarization reconfigurable antenna element in two orthogonal directions V and H and arbitrary projection directions. like Figure 5 As shown, each polarization-reconfigurable antenna element supports individually configurable polarization rotation angles, which are pre-fixed based on the polarization beamwidths of the polarization-reconfigurable array in the X and Y directions. Specifically, this rotation angle is mechanically controlled. The additional rotation angle allows the antenna array greater freedom in configuring beam polarization angles under complex XPD distributions in multipath clusters. This rotation angle is pre-fixed based on the size of the UPA, i.e., the polarization beamwidths in the X and Y directions.

[0025] For mMIMO systems, the receive and transmit antenna gains depend on the array antenna size and the gain of individual antenna elements. For simplicity and without loss of generality, we assume that an ideal omnidirectional antenna is used as the array element. Therefore, for an array using N elements with the same polarization, the array response of a uniform planar array (UPA) can be expressed as follows: ; in λ It is the signal wavelength. d It is the antenna spacing. and Indicates elevation angle and azimuth angle. and For the element index, its value range is as follows: and The corresponding UPA is shaft and Index along the axis. Antenna array size is... ,in and UPA in and Number of units in the direction.

[0026] It should be noted that the AP first uses a digital beamformer to digitally modify the data stream in the baseband to obtain the baseband beamforming matrix. Then, the processed signal is up-converted to the carrier frequency through the RF chain, and then processed by an analog beamformer to obtain the analog beamforming matrix.

[0027] To improve system flexibility and suppress interference, we adopt an AP-UE clustering strategy. Based on a user-centric deployment approach, each AP is connected to the central processing unit via a backhaul link. Simultaneously, considering the characteristics of user clustering, the... Each user is a subset of all APs. In a shared service scenario, after the AP cluster is formed, the subset of UEs served by each AP is represented as follows: m , Total number of users This represents the total number of APs.

[0028] Assume AP m Clustered services for UE k It sends to the UE k The precoded signal is: ; in It is sent to the UE k The data symbol, and satisfying E[| | 2 =1. It is AP m The above corresponds to UE k Digital baseband pre-encoder. It is AP m The analog RF beamformer is implemented using an analog phase shifter, therefore each element satisfies the constant mode constraint, i.e. Its structure consists of AP m The polarization mask matrix Bm is determined.

[0029] Typically, the design of hybrid beamforming is based on The special structure, in this article, The element distribution is related to the polarization distribution of the array and changes with different mask matrices B. This paper assumes AP m Limited UE service capability, denoted as K m One UE, this K m It is a constraint parameter calculated jointly by multiple factors such as the AP's computational load and network overhead. For example, assuming that each AP can only serve a maximum of K / 2 UEs, then each The form is: ; in, Indicates the first One user; , Total number of users =K, Maximum service per AP One UE. The polarization distribution of the user's polarization reconfigurable array V(H) is given, where the polarization reconfigurable array V(H) includes a polarization reconfigurable array with polarization dimension V and a polarization reconfigurable array with polarization dimension H. It should be noted that, for users Its received signal It can be divided into three parts. The first part is the user. The useful signals needed, the second part is the remaining users For users The third part of the interference is noise interference.

[0030] Therefore, UE k Received signal This can be represented as the superposition of all signals sent from the AP to the UE after propagation through the channel, plus additive white Gaussian noise. : ; in, For users The received signal, To serve the first The AP set of each UE m For the first A subset of UEs served by an AP. Total number of users This represents the total number of APs. Indicates the first AP Polar subarray and Millimeter-wave polarization channels between polarized sub-users For other users The original data symbol vector, For other users The corresponding baseband beamforming matrix, It has a mean of zero and a covariance of A circularly symmetric Gaussian distributed additive noise vector.

[0031] Step S11: Based on the signal model, construct the corresponding channel model; It should be noted that millimeter-wave cell-free channels are characterized by high path loss and high spatial correlation. Statistically, some directions within the millimeter-wave channel are more likely to contain signal components than others, which limits spatial selectivity or scattering. Furthermore, due to the shorter wavelength of millimeter waves, we typically neglect the factor of scattering clusters.

[0032] Specifically, based on the aforementioned signal model, the scattering clusters for each user are obtained, and each scattering cluster has several independent multipath components. An initial millimeter-wave Cell-Free MU-mMIMO polarization model is constructed, as shown in the following formula: ; in, The number of clusters per UE, and each cluster has An independent MPC. It is the first The th cluster The complex gain of the path follows a complex Gaussian distribution and includes path loss and shadow fading effects. and These are the normalized receive and transmit array steering vectors, respectively. The first and second hands of user k are respectively The th scattering cluster in the th scattering cluster The azimuth and elevation angles of the multipath components.

[0033] Based on the scattering clusters and independent multipath components, and the signal characteristics of polarization at the scale of the scattering clusters, the polarization distribution matrix is ​​constructed as follows: ; in, [1, ], [1, ]. Indicates the number of clusters, This indicates the number of MPCs in each cluster. This indicates the phase between the TX component and the user component, which is uniformly distributed between [-π, π]. and These represent the cross-polarization discrimination rates at the transmitter with H-polarization and V-polarization dimensions, respectively, and follow the mean value. and standard deviation is The log-normal distribution The imaginary unit; Based on the initial millimeter-wave Cell-Free MU-mMIMO polarization model and the polarization distribution matrix, a channel model is constructed, as shown in the following formula: ; After introducing the polarization of the scattering cluster, the steering vector is no longer of size 1. A vector of ×1 will differ even within the same cluster and under the same independent multipath component when the polarization distribution of the transmitter array is different.

[0034] Step S12: The AP-UE clustering matrix A is obtained by using a greedy optimization algorithm with random annealing, and an initial hybrid beamforming model is constructed.

[0035] Specifically, by calculating the channel gain of each AP-UE pair, the AP with the best channel conditions is prioritized for allocation to the UE. If the AP's connection limit is reached, the AP with the second-highest channel gain is then allocated to ensure that no UE is isolated from the system. Next, the channel gains are sorted in descending order, and connections are established sequentially, while ensuring that the number of UEs per AP does not exceed the capacity limit. Finally, the optimal AP-UE clustering matrix is ​​output.

[0036] Specifically, this article assumes With limited UE service capabilities, This indicates the maximum number of UEs that can access the network. It is a dynamic constraint parameter calculated jointly by multiple factors such as AP's computing load and network overhead; Based on this, we propose a greedy optimization algorithm with random annealing, the principle of which is: By calculating the channel gain of each user-access point pair, the user is preferentially assigned the access point with the best channel conditions. If the number of connections of the AP has reached a certain threshold... If the upper limit is reached, the AP with the second highest channel gain will continue to be allocated to ensure that no UE is isolated from the system. Next, the channel gains are sorted in descending order and connections are established sequentially. Except for the optimal AP, other APs are connected using a probabilistic approach, with the access probability for each AP being: ; in, Let be a random distribution function in the interval 0-1. for and Channel quality information between them for The current AP sub-cluster set.

[0037] Based on a user-centric deployment approach, each AP is connected to the central processing unit via a backhaul link. Simultaneously, considering the clustered nature of users, the... Each user is a subset M of all APs k If {1,…,M} serve each other, then after the AP cluster is formed, the subset of UEs served by each AP is represented as K. m {1,…,K}, Total number of users This represents the total number of APs.

[0038] Furthermore, for each UE's AP cluster, this embodiment of the invention proposes a distributed hybrid polarization beamforming algorithm within the cluster. APs within the same cluster aggregate their received CSI data to the CPU via the fronthaul network, and the CPU performs the following unified beamforming design on all APs.

[0039] The CPU employs the same design approach as the hybrid polarization beamforming of a single-point AP, dividing the beamforming design into two stages: analog beamforming in the radio frequency (RF) domain and digital beamforming in the baseband domain. The analog beamformer is designed to align with the channel phase while incorporating a polarization mask matrix to dynamically adjust the polarization distribution. The digital beamformer uses the minimum mean square error (MMSE) method to mitigate multi-user interference.

[0040] Simulated beamformer Constructed as a block diagonal matrix, where each block corresponds to Corresponding AP m : For each AP m ,initial The design is based on assigning two RF chains (one for V-polarization and one for H-polarization) to each service's users.

[0041] Adding a polarization mask matrix to the hybrid beamforming algorithm The selection effect, then the V-polarization and H-polarization RF chain column vectors of the k-th user are designed as follows: ; ; in, For users Channel phase with AP m, and V and H are polarization mask vectors, respectively. and The One element, , These represent the number of V-polarized and H-polarized antennas, respectively.

[0042] Digital beamformer The design adopts the global MMSE criterion, and first constructs the equivalent channel. : ; The global MMSE digital precoder is: ; in This is the normalization factor. for The conjugate transpose of . Then... Classified by AP It is used for baseband processing of each AP.

[0043] It should be noted that by designing complementary polarization mask matrices, the two polarization reconfigurable arrays (V polarization dimension and H polarization dimension) can be precisely divided in terms of quantity and spatial location, thereby reflecting the specific form of the array polarization distribution.

[0044] like Figure 6 As shown, the polarization distribution of the array is represented by a polarization mask matrix B. Element 1 represents the H polarization dimension, and element 0 represents the V polarization dimension. The V polarization dimension is the vertical polarization dimension, and the H polarization dimension is the horizontal polarization dimension. The V polarization dimension and the H polarization dimension are distinguished by inverting the polarization mask matrix.

[0045] Step S13: Based on the channel model, optimize the channel model with the polarization distribution of the polarization reconfigurable array, various beamforming parameters, and AP-UE clustering matrix A as constraints, and with optimal sum and rate as the objective.

[0046] The process involves calculating the user's received signal to interference plus noise ratio, and based on this ratio, calculating the sum rate using the following formula: ; The achievable sum rate of the system is ; in, For users The ratio of received information to interference plus noise. For noise power, For and rate; Using the polarization distribution of the antenna array, beamforming parameters, and the A matrix as constraints, and with optimal sum and rate as the objective, the millimeter-wave Cell-Free MU-mMIMO polarization model is optimized, as shown in the following formula: P: ; st ; ; ; ; in, ( ) represents the number of antennas in the V(H) polarimetric subarray. This represents the number of V / H polarized users, and B represents the polarization mask matrix, i.e., the polarization distribution of the mask matrix. and Let the constant modes and connectivity constraints of the V-polarized and H-polarized subarrays at the transmitter end be represented respectively. Each V-polarized antenna of the AP is connected to the first The V-RF chains are connected by a phase shifter, at which point the amplitude is constant. , This means that each user is served by at least one AP. This means that the number of users served by each AP cannot exceed the maximum number of UEs that can be accessed by the current AP.

[0047] Step S14: Based on the optimal perturbation genetic algorithm, iteratively and jointly optimize the AP-UE clustering matrix A, the polarization mask matrix B of all APs, and the hybrid beamforming model to obtain the target joint optimization model.

[0048] Specifically, Q and E represent the population size and iteration number, respectively, and each chromosome represents a polarization mask matrix B and a clustering matrix A. The chromosome encoding is in a concatenated form. ; The formula for calculating the population is as follows: ; in, Indicates the first The population in the next iteration, yes The Middle The 4th chromosome, i.e., the 1st chromosome A polarization mask matrix, assuming For the first The chromosome with the highest fitness in the next iteration is the one with the highest fitness polarization mask matrix B and clustering matrix A. ; In the vicinity of the chromosome with the highest fitness, any new chromosome can be generated through random mutation, as shown in the following expression: ; in, The sequence number representing the gene. express Inverse of set It is a range in [1, Randomly generated within ] It consists of a set of distinct numbers, when... After executing the formula Q times, the resulting new population is as follows: ; in, It is a population On the chromosome with the highest fitness The result of nearby mutations, if chromosome fitness Higher than chromosomes fitness If so, the corresponding chromosome needs to be replaced, as shown below: = ; Based on the aforementioned optimal perturbation genetic algorithm, the optimal clustering matrix A, polarization mask matrix B, and hybrid beamformer are iteratively generated, thereby obtaining the target joint optimization model.

[0049] It should be noted that iterative optimization using the Optimal Perturbation Genetic Algorithm (OPGA) ensures model diversity and prevents convergence to local optima. When the termination condition is met, the polarization mask matrix with the optimal fitness value represents the solution to the optimization problem.

[0050] Simulation test Assuming the number of independent multipath components (MPCs) is 6 and the antenna spacing is... The arrival and departure azimuth and elevation angles of the array both follow a Laplace distribution, with the average angle at... The antennas are uniformly distributed within the antenna, with an angle extension of 10°. The number of users per antenna is K=8 and M=10 APs. The number of antennas per AP... =144, =8. For the polarization data of the scattering clusters, we used an indoor, indirect light environment, which means... =10 and =4. In designing hybrid beamforming algorithms, fixed polarization refers to an array polarization distribution in which H-polarized array elements and V-polarized array elements appear alternately.

[0051] like Figure 7 As shown, the system performance comparison is performed after introducing the AP-UE clustering matrix A. The results show that the clustering scheme significantly outperforms the unclustered scheme across the entire SNR range, especially in the high SNR region where the performance gap widens further. A well-designed matrix A can effectively achieve load balancing and channel matching, avoiding overloading of some APs while leaving others idle, thereby maximizing the sum and rate under limited RF chain constraints.

[0052] like Figure 8 As shown, the performance of different hybrid beamforming models under a fixed polarization mask matrix is ​​tested. The proposed global MMSE hybrid precoder performs close to that of the all-digital beamforming. When the analog precoder... When phase-aligned, global MMSE can effectively utilize cross-AP cooperation to suppress multi-user interference, thereby achieving near-all-digital spectral efficiency in a hybrid architecture.

[0053] In the model of this embodiment, F RF With elements randomly distributed, this algorithm can take the polarization distribution into account in the hybrid beamforming design, ultimately resulting in excellent spectral efficiency.

[0054] like Figure 9 As shown, the performance of the hybrid beamforming model with different optimization algorithms in terms of sum rate and number of iterations was compared and tested. The simulated population size was 50, the crossover probability Pc was 0.8, and the mutation probability P... m The values ​​are 0.1, representing the Genetic Algorithm (GA), the Optimal Perturbation Genetic Algorithm (OPGA), the Random Combination Algorithm (RC), and the Fixed Polarization System, respectively. In the Fixed Polarization System, the H polarization dimension and the V polarization dimension are alternately distributed.

[0055] The results show that all polarization optimization algorithms outperform the fixed polarization system, significantly improving system performance and rate. This performance improvement is mainly attributed to the optimization algorithms dynamically adjusting the polarization distribution of the transmit array during iteration, thereby fully utilizing polarization mismatch characteristics to reduce multi-user interference under cross-polarization conditions. To evaluate the upper limit of algorithm performance, we used 2 million random searches to estimate the theoretical optimal value (upper bound) under the same constraints. The OPGA algorithm is only 2% away from this upper bound, proving that it is close to the global optimum under current hardware conditions. In addition, the synergistic optimization of the polarization and spatial domains brings dual benefits: 1) heterogeneous polarization RF chains achieve channel diversity and interference orthogonal suppression; 2) sparse interleaved masks effectively suppress grating lobes. Due to the overlap of AP coverage areas, cross-polarization interference (VH, HV) is stronger in Cell-Free systems; the dynamic polarization mask {Bm} can achieve more significant system gains than single-site mMIMO. In summary, using the Optimal Perturbation Genetic Algorithm (OPGA) to optimize the polarization mask matrix to adjust the overall polarization distribution of the array will effectively improve the performance of the hybrid beamforming model.

[0056] In summary, this embodiment achieves joint optimization of a user-centric cellular-free MIMO system, and simulation experiments demonstrate the feasibility and good convergence performance of the method in this embodiment.

[0057] Example 2 A second embodiment of the present invention provides a joint optimization system for a user-centric cellular-free MIMO system, the system comprising: Equipped with modules for building the downlink of the UCCF millimeter-wave mMIMO communication system, the mMIMO communication system includes multiple randomly distributed access points (APs) and user equipment (UEs), each AP is equipped with a polarization reconfigurable array, and a signal model is built based on the polarization reconfigurable array; A construction module is used to construct a corresponding channel model based on the signal model; The clustering module is used to obtain the AP-UE clustering matrix A using a greedy optimization algorithm with random annealing, and to construct the initial hybrid beamforming model; An optimization module is used to optimize the channel model based on the channel model, with the polarization distribution of the polarization reconfigurable array, various parameters of beamforming, and AP-UE clustering matrix A as constraints, and with optimal sum and rate as the objective. The iterative module is used to iteratively and jointly optimize the AP-UE clustering matrix A, the polarization mask matrix B of all APs, and the hybrid beamforming model according to the optimal perturbation genetic algorithm, so as to obtain the target joint optimization model.

[0058] Compared with existing technologies, this invention constructs a UCCF millimeter-wave mMIMO downlink system containing multiple randomly distributed APs and UEs, with each AP equipped with a polarization reconfigurable array, and establishes a signal model and a channel model with a fused polarization matrix. A greedy optimization algorithm based on channel quality is used to obtain the AP-UE clustering matrix A, constructing an initial hybrid beamforming model. With polarization distribution, beamforming parameters, and the clustering matrix as constraints, the optimal perturbation genetic algorithm (OPGA) is used to jointly optimize the AP-UE clustering matrix A, the polarization mask matrix B of all APs, and the hybrid beamforming model, maximizing system performance and rate. This avoids the problems of insufficient personalized user services, poor inter-user interference management, low resource allocation efficiency, and poor adaptability to high-density / dynamic scenarios in traditional cell-free architectures. It also compensates for the performance bottlenecks caused by high path loss, susceptibility to obstruction, and lack of joint optimization of polarization and clustering in millimeter-wave systems, ultimately achieving improved system spectrum utilization efficiency, effective suppression of multi-user interference, and coordinated optimization of load balancing and personalized services. It solves the problem of limited performance of existing cell-free large-scale MIMO systems in dense user or dynamic network environments.

[0059] Example 3 A third embodiment of the present invention provides a storage medium storing computer instructions that, when executed by a processor, implement the steps of the method described in the first embodiment. Example 4 A fourth embodiment of the present invention provides an apparatus including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method described in the first embodiment.

[0060] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0061] Those skilled in the art will understand that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable instructions for implementing logical functions, and can be embodied in any computer-readable storage medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable storage medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0062] More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable storage media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0063] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0064] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the 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.

[0065] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. A joint optimization method for a user-centric cellular-free MIMO system, characterized in that, The method includes: The downlink of the UCCF millimeter-wave mMIMO communication system is constructed. The mMIMO communication system includes multiple randomly distributed access points (APs) and user equipment (UEs). Each AP is equipped with a polarization reconfigurable array. A signal model is constructed based on the polarization reconfigurable array. Based on the signal model, a corresponding channel model is constructed; A greedy optimization algorithm with random annealing is used to obtain the AP-UE clustering matrix A, and an initial hybrid beamforming model is constructed. Based on the aforementioned channel model, the channel model is optimized with the polarization distribution of the polarization reconfigurable array, various beamforming parameters, and the AP-UE clustering matrix A as constraints, and with optimal sum and rate as the objective. Based on the optimal perturbation genetic algorithm, the AP-UE clustering matrix A, the polarization mask matrix B of all APs, and the hybrid beamforming model are iteratively jointly optimized to obtain the target joint optimization model.

2. The joint optimization method for a user-centric non-cellular MIMO system according to claim 1, characterized in that, The downlink of the UCCF millimeter-wave mMIMO communication system is constructed. The mMIMO communication system includes multiple randomly distributed access points (APs) and user equipment (UEs). Each AP is equipped with a polarization reconfigurable array. The steps for constructing a signal model based on the polarization reconfigurable array include: Each AP employs a single-port polarized reconfigurable antenna element, and the vertical RF array group of the polarized reconfigurable antenna element... and horizontal radio frequency array group The hybrid polarization section forms a polarization reconfigurable array, and the polarization state of the unit is switched by a switch / parasitic element circuit to achieve dynamic adjustment of the polarization distribution; Each polarization reconfigurable antenna element controls the shutdown and power distribution of each polarization stub through a circuit structure with an RF switching circuit, thereby enabling the polarization selection of the polarization reconfigurable antenna element in the two orthogonal directions V and H and in any projection direction. Each polarization reconfigurable antenna element supports individual configuration of the polarization rotation angle, which is pre-fixed by the polarization beamwidth of the polarization reconfigurable array in the X and Y directions.

3. The joint optimization method for a user-centric non-cellular MIMO system according to claim 2, characterized in that, The formula for constructing the signal model based on the polarization reconfigurable array is as follows: Transmitted signal vector Represented as: ； in, Is sent to the Data symbols of a UE, and satisfying E[| | 2 ] =1, It is the first The AP corresponds to the first Digital baseband precoder for each UE express A matrix space of U rows and U columns, It is the first Analog RF beamformer for an AP This refers to the number of antennas at the transmitting end. for and The sum of the number of radio frequency chains, It is a vertical radio frequency array group. It is a horizontal radio frequency array group; ； in, Indicates the first One user; , Total number of users Maximum service per AP One UE, The polarization distribution of the user's polarization reconfigurable array V(H) is given, where the polarization reconfigurable array V(H) includes a polarization reconfigurable array with polarization dimension V and a polarization reconfigurable array with polarization dimension H. No. UE received signal This is represented as the superposition of all signals sent from the AP to the UE after propagation through the channel, plus additive white Gaussian noise. : ； in, For users The received signal, To serve the first The AP set of each UE m For the first A subset of UEs served by an AP. Total number of users This represents the total number of APs. Indicates the first AP Polar subarray and Millimeter-wave polarization channels between polarized sub-users For other users The original data symbol vector, For other users The corresponding baseband beamforming matrix, It has a mean of zero and a covariance of A circularly symmetric Gaussian distributed additive noise vector.

4. The joint optimization method for a user-centric non-cellular MIMO system according to claim 3, characterized in that, Based on the aforementioned channel model, and using the polarization distribution of the polarization reconfigurable array, beamforming parameters, and the AP-UE clustering matrix A as constraints, the steps for optimizing the channel model with optimal sum and rate as the objective include: Calculate the ratio of the user's received signal to the interference plus noise (INF) ratio. Based on this INF ratio, the summation rate is: ； in, For users The ratio of received information to interference plus noise. For the sum and rate, Total number of users; Using the polarization distribution of the polarimetric reconfigurable array, various beamforming parameters, and the AP-UE clustering matrix A as constraints, and with optimal sum and rate as the objective, the channel model is optimized as follows: P: ； s.t. ; ； ； ； in, ( ) represents the number of antennas in the V(H) polarimetric subarray. This represents the number of V / H polarized users, and B represents the polarization mask matrix, i.e., the polarization distribution of the mask matrix. and Let the constant modes and connectivity constraints of the V-polarized and H-polarized subarrays at the transmitter end be represented respectively. Each V-polarized antenna of the AP is connected to the first The V-RF chains are connected by a phase shifter, at which point the amplitude is constant. , This means that each user is served by at least one AP. This means that the number of users served by each AP cannot exceed the maximum number of UEs that can be accessed by the current AP.

5. The joint optimization method for a user-centric non-cellular MIMO system according to claim 4, characterized in that, The steps for obtaining the AP-UE clustering matrix A using a greedy optimization algorithm with stochastic annealing and constructing the initial hybrid beamforming model specifically include: By calculating the channel gain of each AP-UE pair, the AP with the best channel conditions is allocated to the UE first. If the AP is already full, the AP with the second best channel gain is then allocated. After sorting the channel gains in descending order, connections are established sequentially, while ensuring that the number of UEs in each AP does not exceed the capacity limit. The final output is the AP-UE cluster matrix A.

6. The joint optimization method for a user-centric non-cellular MIMO system according to claim 5, characterized in that, The steps for constructing an initial hybrid beamforming model specifically include: Building a simulated beamformer and digital beamformer The initial hybrid beamforming model is composed of; Simulated beamformer It is used to align with the channel phase and incorporates the polarization mask matrix B to dynamically adjust the polarization distribution; Simulated beamformer Constructed as a block diagonal matrix: ； For each AP, initially By assigning two RF chains to each service user, one for V polarization and one for H polarization; Adding a polarization mask matrix to hybrid beamforming The selection effect, then the RF chain column vector design of the V-polarization and H-polarization of the k-th user is as follows: ； ； in, For the first The user and the first Channel phase between APs and V and H are polarization mask vectors, respectively. and The One element, , These represent the number of V-polarized and H-polarized antennas, respectively. Digital beamformer The design adopts the global MMSE criterion, and first constructs the equivalent channel. : ； The global MMSE digital precoder is: ； in, As the normalization factor, for The conjugate transpose of the conjugate will Classified by AP It is used for baseband processing in each AP.

7. The joint optimization method for a user-centric non-cellular MIMO system according to claim 6, characterized in that, The steps for iteratively and jointly optimizing the AP-UE clustering matrix A, the polarization mask matrix B of all APs, and the hybrid beamforming model according to the optimal perturbation genetic algorithm to obtain the target joint optimization model specifically include: Let Q and E represent the population size and the number of iterations, respectively. Each chromosome represents a polarization mask matrix B and a clustering matrix A. The chromosome encoding is in a concatenated form. ； The formula for calculating the population is as follows: ； in, Indicates the first The population in the next iteration, yes The Middle The 4th chromosome, i.e., the 1st chromosome A polarization mask matrix, assuming For the first The chromosome with the highest fitness in the next iteration is the polarization mask matrix B and the clustering matrix A with the highest fitness. The optimal clustering matrix A, polarization mask matrix B, and hybrid beamforming model are generated iteratively to obtain the joint optimization model of the target.

8. A joint optimization system for a user-centric cellular-free MIMO system, characterized in that, The system includes: Equipped with modules for building the downlink of the UCCF millimeter-wave mMIMO communication system, the mMIMO communication system includes multiple randomly distributed access points (APs) and user equipment (UEs), each AP is equipped with a polarization reconfigurable array, and a signal model is built based on the polarization reconfigurable array; A construction module is used to construct a corresponding channel model based on the signal model; The clustering module is used to obtain the AP-UE clustering matrix A using a greedy optimization algorithm with random annealing, and to construct the initial hybrid beamforming model; An optimization module is used to optimize the channel model based on the channel model, with the polarization distribution of the polarization reconfigurable array, various parameters of beamforming, and AP-UE clustering matrix A as constraints, and with optimal sum and rate as the objective. The iterative module is used to iteratively and jointly optimize the AP-UE clustering matrix A, the polarization mask matrix B of all APs, and the hybrid beamforming model according to the optimal perturbation genetic algorithm, so as to obtain the target joint optimization model.

9. A readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the joint optimization method for a user-centric non-cellular MIMO system as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the joint optimization method for a user-centric non-cellular MIMO system as described in any one of claims 1 to 7.

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