Research On Massive MIMO Random Access And Channel State Information Feedback Technology

Today is an era of information explosion,with mobile Internet and IoT services becoming more and more popular.With the increasing number of users,the demand for the speed of wireless data transmission and system capacity has shown an exponential growth trend for mobile data services.An important component of the 5G technology for 2020—Massive Multiple-Input Multiple-Output,which can effectively improve spectral efficiency by using low-complexity linear precoding to simultaneously serve multiple user equipments.However,although this new concept brings many benefits,it also brings new challenges and has drawn the attention of industry and academia.This thesis mainly focuses on the Massive MIMO scenarios,and conducts research work on random access pilot allocation and CSI feedback:1.Aiming at the traditional scheme of centralized resolution of pilot collisions on the BS side,a new method for distributed resolution of pilot collisions on the UE side is proposed.The traditional method needs to allocate pilot sequences to each UE before sending payload data.The proposed method determines whether the pilot is repeatedly transmitted by the UE according to the strength of the signal,to avoid pilot collision in the cell and implement the payload transmission situation which considered in the main body of the large-scale MIMO research.The simulation results show that the UE-side distributed resolution of pilot collision can effectively improve the probability of resolving conflicts and reduce the number of random accesses.2.A method for performing limited CSI feedback through D2 D communication in a large-scale MIMO scenario which in FDD mode is proposed.After obtaining the optimal bit partition on each D2 D link,the CSI exchange is performed,then the precoding is performed at the user end,and the pre-coded information is fed back to the base station,which can significantly improve the CSI feedback efficiency.Compared with the traditional method,the simulation results show that the D2D-based CSI feedback can effectively improve the utilization ratio of frequency band.3.An adaptive sparse channel estimation and CSI limited feedback method combined with compressed sensing in massive MIMO systems in FDD mode is proposed.The proposed limited feedback method uses a double directional(DD)MIMO channel to construct a double directional dictionary model which based on the angle of departure(AOD)and angle of arrival(AOA),while estimating and reconstructing the channel using the OMP-SQ algorithm.Even if the number of transmit antennas in the BS is large,the algorithm uses a small number of feedback bits to provide acceptable channel estimation accuracy.The simulation results show that the proposed OMP-SQ algorithm can achieve smaller average normalized mean square error(NMSE)and higher total link capacity than the OMP-VQ algorithm.