Channel Estimation Algorithm for OTFS System Based on Bayesian Learning
A novel channel estimation technique based on sparse Bayesian learning(SBL)framework is proposed for Orthogonal Time Frequency Space(OTFS)systems.Considering that the number of scatterers in the transmission environment is usually limited,the original delay-doppler(DD)domain channel response exhibits sparse behavior.So the channel estimation issue is formulated as a one-dimensional(1D)off-grid sparse signal recovery(SSR)problem based on a virtual sampling grid defined in the DD space and re-solved by using the proposed Perturbed Sparse Bayesian Learning(PSBL)method.In particular,the linear interpolation method is used to approximate the real observed matrix,and then the expectation-maximiza-tion(EM)method is used to jointly estimate the sparse vector and off-grid components.Simulation results demonstrate that the proposed algorithm has better channel estimation performance and lower pilot load rate,which is superior to the channel estimation method based on greedy algorithm.
OTFSchannel estimationsparse signal recoverycompressed sensing
万方数据
维普
