Adaptive Pattern-Coupled Sparse Bayesian Learning for Channel Estimation in OTFS Systems

Abstract

The orthogonal time frequency space (OTFS) has emerged as a promising modulation waveform for high-mobility wireless communications owing to its robust advantages of resisting Doppler effects. However, due to the limit of the frame duration, the fractional Doppler shift appears, which is a challenge for channel estimation in OTFS systems. In this letter, we formulate the channel estimation problem as a block sparse signal recovery issue and propose an adaptive pattern-coupled sparse Bayesian learning (APCSBL) method. To be specific, we introduce a pattern-coupled hierarchical Gaussian prior model to characterize the dependencies among adjacent channel coefficients. On this basis, an adaptive hyperparameter strategy is presented, in which we appropriately utilize various coupling parameters further to characterize the strength of the correlation between adjacent elements. Then we exploit the expectation maximization (EM) algorithm to update the hidden variables and the channel vector. Simulation results demonstrate that the proposed algorithm outperforms existing methods and works for various environments.