| With the rapid development of the mobile Internet and the Internet of Things,the development of mobile communication technology is also rapidly and iteratively updated.With the formal commercialization of 5G mobile communication systems,Multiple-Input Multiple-Output(MIMO)technology is one of the most effective means to greatly improve the spectrum utilization efficiency while ensuring power efficiency.Using macro diversity,large-scale distributed MIMO can further improve transmission performance through cooperation,which has become a research hotspot of MIMO technology.Specifically,our main contributions are summarized as follows:Chapter 1 introduces the research background of the thesis firstly.Then we introduce the development overview of massive MIMO technology and large-scale distributed MIMO technology.Finally,the research status of multi-user precoding and multi-user detection in large-scale distributed MIMO systems is described,and the research work and content arrangement are summarized.Chapter 2 studies the deterministic equivalence derivation of downlink ergodic sum-rate in large-scale distributed MIMO systems.First,considering the difficulty to obtain accurate Channel State Information(CSI)in actual systems.Considering the phase rotation problem in the frequency domain caused by the different distances between users and multiple access points,we introduce phase rotation variables and precoding compensation variables and derive the ergodic sum-rate based on the imperfect CSI.Finally,based on the large dimensional random matrix theory(RMT),we analyze the deterministic equivalent of ergodic sum-rate of a downlink system using regularized zero-forcing(RZF)precoding.Simulation results show that the deterministic equivalent can provide reliable performance prediction with lower computational cost.Chapter 3 studies the design of precoding compensation matrix in large-scale distributed MIMO system.According to the deterministic equivalent of the derived ergodic sum-rate,we use the optimization method to design the precoding compensation matrix.Then we propose an iterative local optimization algorithm to tackle the nonconvexity of the problem.The framework of sequential parametric convex approximation(SPCA)method is used in our algorithm,which has proven to be an effective tool for numerical solutions of nonconvex optimization problems.Simulation results show that the optimized precoding compensation matrix can effectively cancel the interference regardless of the low SNR or high SNR.In addition,the precoding matrix and the precoding compensation matrix only need to be calculated once within the coherent bandwidth.The precoding compensation matrix of other subcarriers can be generated by rotating the calculated compensation matrix.In chapter 4,we study the low-complexity iterative soft-input soft-output(SISO)detection algorithm in a large-scale distributed multiple-input multiple-output(MIMO)system.The uplink interference suppression matrix is designed to decompose the received multi-user signal into independent single-user receptions.An improved minimum-mean-square-error iterative soft decision interference cancellation(MMSE-ISDIC)based on eigenvalue decomposition(EVD-MMSE-ISDIC)is given to perform low-complexity detection of the decomposed signals.Furthermore,two iteration schemes are given to improve receiving performance,which are iterative detection and decoding(IDD)scheme and iterative detection(ID)scheme.While IDD utilizes the external information generated by the decoder for iterative detection,the output information of the detector is directly exploited with ID.In particular,we introduce the construction and structure model of a large-scale distributed MIMO prototyping system based on general purpose processor(GPP).Finally,the system was used to verify the proposed schemes.The experimental results show that the proposed iterative receiver greatly outperforms the linear MMSE receiver,since it reduces the average number of error blocks of the system significantly. |
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