Research On Key Technologies Of Broadband Channel Estimation Based On Compressed Sensing

As an important part of B5G technology,high frequency channel estimation has attracted more and more attention.Aiming at the characteristics of sparse channel,compressed sensing algorithm is the key technology of sparse signal reconstruction,and its related research has very important practical significance.This paper focuses on the measurement matrix design and signal recon-struction algorithm in compressed sensing algorithm,and studies the low correlation measurement matrix modeling between columns and the optimization algorithm based on greedy reconstruction.The main contributions of this academic dissertation are as follows:Firstly,aiming at the problem of channel estimation performance degradation caused by the correlation between measurement matrix columns,this paper proposes a piecewise column correla-tion matrix structure model,and simulates the measurement matrix constructed based on this mod-el.The results show that the measurement matrix can effectively reduce the correlation between columns and improve the channel estimation accuracy compared with the traditional measurement matrix.In addition,inspired by the structural characteristics of the correlation matrix of piecewise columns,this paper proposes a grouping parallel reconstruction algorithm based on pre-selection set.Among them,the adaptation model of the correlation matrix structure between the preselected set and the segmented column is mainly studied.At the same time,the structure of grouping par-allelization is proposed according to this model.Then,a group selection support set scheme based on correlation is given.Simulation results show that this algorithm can not only effectively shorten the running time,but also improve the channel estimation accuracy as a global processing method.Secondly,aiming at the problem that the traditional greedy reconstruction algorithm cannot effectively eliminate the error index,which leads to the low accuracy of channel estimation,this paper proposes a dynamic feedback matching pursuit(DFMP)algorithm.Among them,the method of dynamically expanding candidate sets by adding feedback sets is mainly studied.And a screening scheme based on dynamic reduction set is proposed.The simulation results show that the channel estimation probability of DFMP algorithm is significantly higher than that of the traditional algorithm,so the algorithm can not only select more potential correct indexes but also dynamically eliminate the error index set with iteration.However,DFMP has the problem of fixed index set selection and too many iterations.In order to ensure the reconstruction accuracy and reduce the running time,this paper proposes a fast dynamic feedback matching pursuit(FDFMP)algorithm.The optimization method based on preset feedback length is studied.And a dynamic selection scheme of index set based on residual tracking is proposed.The simulation results show that the running time and the accurate reconstruction probability of the FDFMP algorithm are improved compared with those of the DFMP algorithm,so the algorithm can reduce the deviation of the index set and accelerate the expansion of the support set.