OTFS Channel Estimation Utilizing Sparse Bayesian Generative Modelling
TL;DR
This work establishes the OTFS-CSGMM framework as a promising solution for high mobility wireless channel estimation by employing a compressive sensing (CS)-based sparse Bayesian generative model (SBGM), namely the recently introduced compressive sensing Gaussian mixture model (CSGMM).
Abstract
One of the key challenges of future wireless communication systems is ensuring reliability in high-speed mobile scenarios, where accurate recovery of channel state information (CSI) is essential. Many recent studies have concluded that orthogonal time-frequency space (OTFS) modulation is a promising technology for addressing this challenge. Additionally, machine learning (ML)-based methods have the potential to improve channel estimation performance by leveraging ambient information more effectively than classical estimation techniques. This paper particularly addresses channel estimation for OTFS by employing a compressive sensing (CS)-based sparse Bayesian generative model (SBGM), namely the recently introduced compressive sensing Gaussian mixture model (CSGMM). We show that our proposed approach yields significant improvement in normalized mean squared error (NMSE) over the next-best-performing baseline. We additionally provide insights into the theoretical potential of the model to optimally approximate complex channel distributions with arbitrary precision within the Doppler-delay (DD) domain. To summarize, this work establishes the OTFS-CSGMM framework as a promising solution for high mobility wireless channel estimation.