Enhancing Channel Estimation for OTFS systems using Sparse Bayesian Learning with Adaptive Threshold

TL;DR

This paper introduces an adaptive Bayesian threshold-based method for channel estimation in OTFS systems, improving noise robustness and reducing complexity by leveraging sparse Bayesian learning in the delay-Doppler domain.

Contribution

It proposes a novel adaptive threshold mechanism within sparse Bayesian learning for OTFS channel estimation, addressing pseudo-peak issues and enhancing performance in low SNR conditions.

Findings

Outperforms existing algorithms in anti-noise performance

Reduces computational complexity

Effectively mitigates pseudo-peak issues in low SNR

Abstract

Orthogonal time frequency space (OTFS) modulation is a two-dimensional modulation scheme designed in the delay-Doppler (DD) domain, exhibiting superior performance over orthogonal frequency division multiplexing (OFDM) modulation in environments with high Doppler frequency shifts. We investigated the channel estimation in the DD domain of OTFS systems, modeling it as a sparse signal recovery problem. Subsequently, within the existing sparse Bayesian learning framework, we proposed an adaptive Bayesian threshold-based active denoising mechanism. Combined with inverse-free sparse Bayesian learning, this effectively addresses the pseudo-peak issue in low signal-to-noise ratio (SNR) scenarios while maintaining low complexity. The simulation results demonstrate that this algorithm outperforms existing channel estimation algorithms in terms of anti-noise performance and complexity.