Research On A New Channel Estimation Algorithm For 6G

The advent of the era of 5G communication means that millimeter wave(mm Wave)technology,large-scale multiple-input multiple-output(MIMO)technology and other key technologies have matured,many scholars at home and abroad gradually turn to 6G mobile communication related research,among which,channel estimation has been a research hotspot,in order to accelerate the research progress of 6G mobile communication,the specific research content of this paper is as follows:(1)Aiming at the special structure problem of signal that is often ignored in most broadband sparse channel estimation schemes,the block sparse signal reconstruction algorithm is obtained by using the block sparse characteristics of the signal,that is,the characteristics of the non-zero elements of the signal being clustered and represented,combined with the Co Sa MP algorithm;In addition,on the basis of ensuring the communication performance of the system,the minimum number of iterations required by the optimization algorithm is calculated to reduce the computational complexity.Based on the traditional Restricted Isometry Property(RIP)criterion,it is proved and extended that the sufficient conditions for algorithm convergence can effectively improve the signal reconstruction efficiency.The simulation results show that compared with other channel estimation algorithms,the block sparse signal reconstruction algorithm has good channel estimation performance,and the application scope is no longer limited to the reconstruction process of general sparse signals.(2)Aiming at the problem that the existing far-field and near-field channel estimation schemes cannot be directly used to accurately estimate the state information of mixed-field channels,an efficient mixed-field channel estimation algorithm based on Co Sa MP algorithm is proposed.The mixed-field channel model is accurately modeled to capture the possible presence of different scatterers in its far-field and near-field regions,and its far-field and near-field path components are estimated,respectively.The simulation results show that compared with other existing channel estimation algorithms,the algorithm can obtain higher normalized mean squared error performance and higher system and speed under the same low pilot overhead,and the calculation complexity is lower.(3)In view of the serious problem of beam splitting effect in wideband THz channel,this is done externally based on the hybrid precoding wideband THz massive MIMO system model to compensate for the gain loss caused by beam splitting in combination with the delay phase precoding control beamformer,and the beam splitting effect characteristics are analyzed from the inside to calculate the physical direction of each path component to obtain the sparse channel support of different subcarriers,and then the beam splitting mode detection optimization algorithm is obtained.The simulation results show that compared with the channel estimation algorithm based on OMP and SOMP,the beam splitting mode detection optimization algorithm has lower complexity,smaller bit error rate and higher accuracy,and the channel estimation performance is significantly improved.To sum up,starting from the goals of improving the transmission environment,improving the accuracy of Channel State Information(CSI)and reducing the computational complexity,this paper designs an improved Co Sa MP algorithm based on block sparse signals and a hybrid algorithm based on Co Sa MP.Both the field channel estimation algorithm and the improved channel estimation algorithm based on beam splitting pattern detection show good channel estimation performance in the simulation results.