Research On Low-complexity Signal Detection Algorithms For Uplink Massive MIMO Systems

As one of the key enabling technologies for the fifth generation(5G)of mobile communications,Massive MIMO helps wireless communication systems achieve significant breakthrough in spectral efficiency,transmission rate,energy efficiency and reliability.However,with the number of antennas increasing,one challeng for achieving these benefits lies in finding practical signal detection algorithms in the uplink system.This thesis focuses on the research of signal detection algorithms with low complexity and high performance for uplink Massive MIMO systems.This thesis first summarizes in detail the state-of-the-art low complexity MMSE detection algorithms and divided them into two categories,namely the matrix inversion approximation and the classical iterative linear equation solving methods.The first category mainly includes Newman series expansion and Newton iteration algorithm;the second category mainly includes Jacobi iteration,Gaussian iteration,Relaxation iteration,Successive Over-Relaxationiteration,Richardson iteration,steepest descent method and conjugate gradient algorithm.All of these algorithms avoid the high complexity matrix inversion required by MMSE detection algorithm,and hence reduce the complexity.After the principle,complexity,and relationships of these algorithms are introduced respectively,the soft output detection scheme for simplified algorithms is given,and then this thesis makes comparisons between each type of the algorithms in terms of performance and computation complexity.Comprehensively considering the advantages and disadvantages of each type of the algorithms,a soft output detection based on joint Neumann series expension and Jacobi iteration is proposed,which makes good trade-off between performance and computational complexity.Furthermore,a lowcomplexitydetection algorithm based on Kaczmarz method is proposed in this paper,which can circumvent the matrix inverseoperation and hence reduce the complexity.Secondly,aiming to solve the problem of performance degradation of existing lowcomplexity MMSE detection algorithms when the number of users increases,based on interference cancellation method,this thesis studies an iterative parallel interference cancellation algorithm for hard-decision massive MIMO systems.Meanwhile,the sorting and noise prediction mechanism are introduced and the noise-prediction aided iterative parallel interference cancellation algorithm is proposed to further improve the detection performance.As far as the residual inter-antenna interference,this thesis propose a low complexity soft output detection algorithm as well.And all of the above proposed signal detection methods can achieve their performance quite close to or even surpassing that of the MMSE algorithm with low computational complexity.Finally,in order to extend the soft-input soft-output detection algorithm into the massive MIMO systems,a lowcomplexity scheme is proposed for MMSE-PIC algorithm,which can effectively reduce the complexity while ensuring performance.The theoretical analysis and simulation results show that the simplified MMSE algorithms based on improved Jacobi iteration and Kaczmarz iterationcan can achieve their performance quite close to that of the MMSE algorithm with only a small number of iterations,and are feasible for massive MIMO systems whose number of antennas at base station is much larger than that of users.With the number of users increasing,the preferences of the simplified MMSE algorithms decrease significantly,and the algorithms based on iterative parallel interference cancellation are more appropriate candidates.When the system performance is considered as the core target with highest priority,this thesis gives the effective solutions of low complexity soft input soft output algorithm.