Joint Multi-Domain Channel Estimation Based on Sparse Bayesian Learning for OTFS System br

Since orthogonal time-frequency space(OTFS) can effectively handle the problems causedby Doppler effect in high-mobility environment, it hasgradually become a promising candidate for modula-tion scheme in the next generation of mobile com-munication. However, the inter-Doppler interference(IDI) problem caused by fractional Doppler posesgreat challenges to channel estimation. To avoid thisproblem, this paper proposes a joint time and delay-Doppler (DD) domain based on sparse Bayesian learn-ing (SBL) channel estimation algorithm. Firstly, wederive the original channel response (OCR) from thetime domain channel impulse response (CIR), whichcan reflect the channel variation during one OTFSsymbol. Compare with the traditional channel model,the OCR can avoid the IDI problem. After that, the di-mension of OCR is reduced by using the basis expan-sion model (BEM) and the relationship between thetime and DD domain channel model, so that we haveturned the underdetermined problem into an overdeter-mined problem. Finally, in terms of sparsity of chan-nel in delay domain, SBL algorithm is used to estimatethe basis coefficients in the BEM without any priori in-formation of channel. The simulation results show theeffectiveness and superiority of the proposed channel estimation algorithm