Research On Millimeter-wave Massive MIMO Channel Estimation Algorithm

With the rapid development of 5th-Generation,Massive MIMO technology has become one of the most promising key technologies in 5th-Generation.However,while Massive MIMO technology could provide reliable performance guarantee,as the number of antennas increases,the problem of channel estimation has also become more and more complicated.In order to solve the problem of channel estimation effectively,this paper proposes a channel estimation algorithm for millimeter-wave Massive MIMO systems.By ultilizing the sparse scattering characteristics of the channel in millimeter waves,the channel is represented as the form in the angle domain,and the channel estimation problem is transformed into the problem of optomal reconstruction of 1l norm or 0l norm.Combined with the related traditional theory of compressed sensing,we use the greedy iterative algorithm OMP and the most Optimize the approximation algorithm BPDN to achieve millimeter-wave channel estimation,which can achieve better channel estimation performance and reduce the complexity.In order to meet the requirement of practical applications,we don't need to know the prior information,such as sparsity,noise variance,etc.We can use sparse Bayesian learning to estimate the channel,and through a small number of iterations and updating,we could get the required channel state information.Next,we consider the angle spread of Ao A and Ao D in the angle domain channel model,and introduce a new structured sparse channel model.we use the BOMP algorithm to estimate the corresponding channel parameters,and finally in the case of lower complexity,obtain the ideal channel state information.Also,we consider the time correlation between the millimeter-wave narrow-band block fading channels,and establish the time state models of channel gain,Ao A,and Ao D.By ultilizing extended Kalman filtering,SIR particle filtering,we can dynamically estimate and track the channel.Finally,by comparing the normalized mean square error of channel estimation,we can conclude that the channel estimation algorithm based on SIR particle filtering can obtain better estimation performance,however,it is obvious that the algorithm has higher complexity compared to the extended Kalman filter.