Beamspace Channel Estimation For Millimeter-wave Massive MIMO Systems Based On Compressive Sensing

In order to content the performance requirements of next-generation mobile communication systems,massive MIMO technology and millimeter-wave has become a key technology for 5G communication systems.Massive MIMO technology utilizes spatial dimensions to significantly increase system capacity and speed.The millimeter wave band is rich in bandwidth resources and short in wavelength,which facilitates the arrangement of base station antennas for Massive MIMO systems.The obtainment of channel state information(CSI)is often a prerequisite for the implementation of various technologies in the transmitter and receiver of the communication system.The accuracy of the CSI estimation directly affects the performance of the communication system.Therefore,as a key technology of communication systems,channel estimation is of great significance for improving system performance.This paper studies the channel estimation in millimeter-wave massive MIMO communication systems.First of all,the background and research status of channel estimation isintroduced in this paper,and expounds the research work and innovation of this paper.Then,the propagation characteristics of the wireless channel,the development of Massive MIMO technology and millimeter wave,as well as the system model are introduced.Then,the theory of compressive sensing theory is introduced,and the performance of conventional linear channel estimation algorithm and compressive sensing algorithm in sparse channel is compared by simulation.Finally,the CoSaMP algorithm and SP algorithm will be applied to the millimeter-wave massive MIMO system.The problems of the two algorithms are analyzed and further improved.The joint block sparse algorithm,the compressed subspace algorithm and the joint iterative support detection algorithm are proposed.The simulation proved that the proposed algorithm performs better.