Channel Estimation For Millimeter-wave MIMO System Based On Lens Antenna Array

The emergence of Millimeter-wave(mm Wave)MIMO system promoted the realization of wireless communication system with low delay,high reliability and high connection number.However,the high complexity of the system increased the difficulty of obtaining channel state information(CSI).At the same time,the traditional system based on linear antenna array has the problem of "Grid Error",which hinders the acquisition of accurate CSI.The system based on lens antenna array can avoid this problem skillfully.It is a promising system,which is also the focus of this thesis.At present,the estimation accuracy of existing channel estimation algorithms is not good,especially in low SNR scenarios.Aiming at the problem,this thesis studied from the following two aspects: Flat fading scenario and frequency selective fading scenario.Firstly,Millimeter-wave MIMO system with hybrid structure was studied.We introduced the mm Wave MIMO channel model and MIMO system architecture.Then,the problem of "Grid Error" existing in mm Wave MIMO system with linear antenna array was analysed,which led to the study of the system based on lens antenna array.Secondly,in the flat fading scenario.For the system based on lens antenna array,Aiming at the problem of low estimation accuracy of traditional algorithms,we proposed a novel algorithm named Support Detection Sparse Bayesian Learning(SDSBL)algorithm.This algorithm can consider the actual characteristics of beamspace channel and the influence of additive noise,which can improve the estimation accuracy effectively,especially in low SNR scenario.Simulation results showed that the proposed algorithm can improve MSE performance and sum-rate performance without significant increasing the computational complexity.Finally,in the frequency selective fading scenario.The proposed SDSBL algorithm was extended to the wideband channel estimation.Simulation results showed that the proposed algorithm was still superior to the existing wideband channel estimation algorithms,but the computational complexity was high.In order to further reduce the complexity of the proposed algorithm,the Inverse Free Sparse Bayesian Learning(IFSBL)algorithm was used to improve the performance of proposed algorithm.The simulation results showed that the improved algorithm can reduce the computational complexity of the original algorithm without significant reduction in estimation accuracy.