This paper proposes a sparse Bayesian learning (SBL)-based channel estimation algorithm for orthogonal time frequency space (OTFS) systems. By introducing the basis expansion model (BEM), we reconstruct the original high-dimensional time-varying channel matrix to a low-dimensional weighted combination of basis functions. Then, we propose a two-stage SBL model that estimates the coefficients of the basis functions by leveraging the sparseness of the channel in the delay-Doppler (DD) domain. Further, an iterative decision feedback scheme is proposed to refine the accuracy of channel estimation and symbol detection. Simulation results show that even with a few iterations, the accuracy of channel estimation can be significantly improved. Besides, the proposed data-aided two-stage SBL-based OTFS channel estimation algorithm outperforms the existing OTFS receivers exactly in terms of estimation mean square error and bit error rate.
BEM Based Channel Estimation via Sparse Bayesian Learning for OTFS over Fast Time-Varying Channel
24.06.2024
1565586 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch