Research On Channel Estimation And Localization Of Millimeter Wave Massive MIMO Systems

Because of the shortage of wireless spectrum resources,millimeter-wave communication has received attention and extensive research in recent years.Millimeter-wave communication can have a wider bandwidth,and can also reduce the size of the antenna array,which has good application prospects.Millimeter-wave communication and massive multiple-input multiple-output(MIMO)system,are all key technologies of 5th-generation(5G).Channel estimation is the first step in wireless communication.In traditional MIMO system,the propagation delay between different antennas is much smaller than the symbol period in millimeter wave communication due to the small number of antennas.In massive MIMO system,however,there is a non-negligible propagation delay between different antennas due to the large-scale antenna array.Aiming at this problem,this paper models the massive MIMO channel on the basis of fully considering the propagation delay between different antennas,and puts forward a two-stage channel estimation algorithm of coarse estimation and fine estimation.In coarse estimation stage,this paper models the channel as the product of a dictionary matrix and a sparse vector,and estimates the channel parameters with the help of sparse Bayesian learning(SBL)and expectation maximization(EM)algorithm.In fine estimation stage,this paper uses Newton's method and least squares(LS)method to iteratively update the parameters on the basis of coarse estimation.Accurate location information is essential for many applications in wireless networks.After channel estimation,the location of user can be estimated based on channel parameters.In this part,this paper also uses a two-stage algorithm of coarse estimation and fine estimation to estimate the location of the user.Firstly,the channel parameters of line of sight(Lo S)are used to make a coarse estimate of the user's location,and other location parameters are obtained according to geometric relationship.Then the mapping between location parameters and channel parameters is used to convert the position parameter to its corresponding channel parameter,and minimize the residuals of the mapped channel parameters and the estimated channel parameters to obtain the fine estimation.This paper also analyzes the performance limits of channel parameters estimation and location parameters estimation.After calculating the Cramér–Rao Bound(CRB)of the channel parameters,this paper subtly converts the CRB of the channel parameters to the CRB of the location parameters with the help of transformation matrix.In this paper,the proposed algorithm is simulated and compared with the CRB of the system.The simulation results show that the performance of the proposed algorithm can reach the theoretical optimal performance above a certain signal-to-noise ratio(SNR),which verifies the rationality of the model and the algorithm.