Nonparametric Variational Bayesian Learning for Channel Estimation with OTFS Modulation

TL;DR

This paper introduces a nonparametric Bayesian framework for OTFS channel estimation that automatically infers multipath components, models clustering, and improves accuracy in high-mobility scenarios for future 6G networks.

Contribution

It presents a novel NPBL approach utilizing a stick-breaking process and Gaussian mixture modeling to enhance OTFS channel estimation by capturing structured sparsity and clustering.

Findings

Achieves lower normalized mean squared error than existing methods.

Effectively infers the number of multipath components automatically.

Reduces computational complexity through spurious component pruning.

Abstract

Orthogonal time frequency space (OTFS) modulation has demonstrated significant advantages in high-mobility scenarios in future 6G networks. However, existing channel estimation methods often overlook the structured sparsity and clustering characteristics inherent in realistic clustered delay line (CDL) channels, leading to degraded performance in practical systems. To address this issue, we propose a novel nonparametric Bayesian learning (NPBL) framework for OTFS channel estimation. Specifically, a stick-breaking process is introduced to automatically infer the number of multipath components and assign each path to its corresponding cluster. The channel coefficients within each cluster are modeled by a Gaussian mixture distribution to capture complex fading statistics. Furthermore, an effective pruning criterion is designed to eliminate spurious multipath components, thereby enhancing estimation accuracy and reducing computational complexity. Simulation results demonstrate that the proposed method achieves superior performance in terms of normalized mean squared error compared to existing methods.