Research On Beamforming For Large Scale Distributed Mimo System

Today,with the release of the fifth generation(5G)mobile communication business package,mobile communication has experienced nearly a century of development and evolution.The new generation mobile communication systems pay more attention to the technical indicators of low delay,high reliability,wide coverage and giant connection.Large-scale distributed multiple-input and multiple-output(MIMO)and cellfree large-scale MIMO systems have become two important network architectures for the research of new generation mobile communication systems,the reason is that they can provide better coverage,stronger antiinterference ability and more flexible networking.With the rapid increase of user terminal equipments,the backhaul consumption of large-scale distributed MIMO and cell-free large-scale MIMO systems increases sharply,the quality of service(Qo S)of each user terminal and the further improvement of system spectral efficiency(SE)are meeting greater challenges.In view of the above problems,we carry out the related technical research.Firstly,aiming to solve the interference problem of cell-edge users in traditional cellular networks,a large-scale distributed MIMO system with interlaced clustering is proposed.In this network,by continuously dividing different clustering patterns(CPs)by orthogonal frequencies,the users at the edge of the cell are located in the center of one of the CPs in a large probability,which is helpful for the edge users in the cell to find relatively favorable channel conditions in the clustering process.In order to optimize the WSR under backhaul and power constraints,a sparse beamforming optimization scheme is proposed.Considering the high computational complexity caused by the ultradensely deployed remote antenna units(RAUs)and a large number of giant connections between user terminals,we decompose the original optimization problem into a two-stage optimization problem.In the first stage,we propose an efficient user selction algorithm to find the largest user subset that satisfies the Qo S requirement.In the second stage,we optimize the original WSR problem for the selected users in the previous stage.In the second stage,there are five different optimization algorithms: CP-based weighted minimum mean square error(CP-WMMSE)algorithm,primal/dual decomposition-based power allocation algorithm for sparse beamforming and fast iterative algorithms for sparse beamforming with primal/dual decomposition.Numerical results show that the proposed algorithms can improve the user performance of cell edge.Secondly,in order to improve the SE of large-scale distributed MIMO,a joint sparse beamforming and power control for a network-assisted full duplex(NAFD)is proposed.To maximize the total SE of both uplink and downlink with Qo S and backhaul constraints,an optimization model is established.By using the method of relaxed integer programming,the original mixed integer programming problem is decomposed into two subproblems: the linearized approximate subproblem of the original problem and the subproblem based on the associated solution of user and RAU.In the first stage,the relaxed approximation subproblem is optimized to obtain the correlation solution set between the user and the RAU.In the second stage,the solution set is brought into the original problem to optimize the uplink power control,downlink transmit sparse beamforming vector and uplink receiver.In addition,for each subproblem,two optimization algorithms with different complexity are proposed,i.e,semi-definite relaxation block coordinate descent(SDR-BCD)method and iterative sequential parametric convex approximation(SPCA)algorithm.The simulation results show that the proposed NAFD scheme can achieves higher SE gain than the traditional time division duplex(TDD)and cloud radio access network(C-RAN)co-frequency co-time full duplex(CCFD)schemes.At the same time,when the cross link interference(CLI)is suppressed to a low level,compared with the TDD scheme,the NAFD and the C-RAN CCFD schemes can obtain higher performance gains.Thirdly,in order to further improve the SE of cell-free large-scale MIMO,a joint duplex mode selection and transceiver design for network-assisted full duplex is proposed.In order to maximize the total SE of both downlink and uplink,we jointly optimize the duplex mode selection,downlink beamforming vector,uplink transmit power and uplink receiver.Since the duplex mode selection parameters,uplink and downlink optimization parameters are highly coupled together,the solution of the problem is challenging.To solve the problem,a two-stage heuristic alternative optimization algorithm is proposed.In the first stage,we optimize transceiver parameters by fixing mode selection parameters;in the second stage,we optimize mode selection parameters by fixing transceiver parameters.In addition,the non-convex functions in the two stages are transformed into convex forms by a series of nonconvex-convex approximate methods,such as equivalent formulations,iterative success convex approximations,and binary relaxations.Numerical results indicate that the proposed duplex mode selection algorithm can achieve SE performance that is close to the optimal exhaustive search solution and yields a higher SE gain compared with the traditional fixed-mode duplex scheme.Finally,in order to guarantee the Qo S of the users should be accepted by the network and to ensure that the optimization problem can be solved,a joint user selection and transceiver design for cell-free large-scale MIMO with NAFD is proposed.Our goal is to jointly maximize the total SE of the uplink and downlink and the number of users that should be admitted by the network.At the same time,Qo S constraints for both uplink and downlink,fronthaul capacity constraints,energy harvesting constraints and simultaneous wireless information and power transfer(SWIPT)ratio design are considered.In order to solve the original optimization problem efficiently,a double-loop optimization method is proposed,that is,the inner loop is to solve the optimization problem of joint user selection and transceiver design to find the users who are least likely to be accepted by the network;the outer loop is to update the candidate user set,and continuously update the user set for the network according to the results of the inner loop.A successive convex approximation(SCA)-based algorithm which can achieve stable convergence and satisfy KKT(karush-Kuhn-Tucker)condition is used to optimize the inner loop.Furthermore,the results show that compared with the C-RAN CCFD and TDD schemes,the proposed NAFD scheme can achieve higher performance gain and the more number of selected users under the most cases.In addition,compared with the C-RAN CCFD scheme,the TDD scheme has better performance in selecting the number of users in low-speed scenarios.