Joint multi-domain channel estimation based on sparse Bayesian learning for OTFS system

Since orthogonal time-frequency space (OTFS) can effectively handle the problems caused by Doppler effect in high-mobility environment, it has gradually become a promising candidate for modulation scheme in the next generation of mobile communication. However, the inter-Doppler interference (IDI) problem caused by fractional Doppler poses great challenges to channel estimation. To avoid this problem, this paper proposes a joint time and delay-Doppler (DD) domain based on sparse Bayesian learning (SBL) channel estimation algorithm. Firstly, we derive the original channel response (OCR) from the time domain channel impulse response (CIR), which can reflect the channel variation during one OTFS symbol. Compare with the traditional channel model, the OCR can avoid the IDI problem. After that, the dimension of OCR is reduced by using the basis expansion model (BEM) and the relationship between the time and DD domain channel model, so that we have turned the underdetermined problem into an overdeter-mined problem. Finally, in terms of sparsity of channel in delay domain, SBL algorithm is used to estimate the basis coefficients in the BEM without any priori information of channel. The simulation results show the effectiveness and superiority of the proposed channel estimation algorithm.