Nonparametric Variational Bayesian Learning for Channel Estimation with OTFS Modulation

2026-02-11

AI Summary

Existing OTFS channel estimation methods fail to fully exploit the structural sparsity and clustering characteristics inherent in clustered delay-line (CDL) channels, leading to performance limitations. This work proposes a novel nonparametric Bayesian learning framework for OTFS channel estimation, introducing a stick-breaking process to adaptively infer the number of multipath components and perform automatic clustering. The intra-cluster fading statistics are modeled using a Gaussian mixture model, and a multipath pruning mechanism is devised to enhance both estimation accuracy and computational efficiency. By effectively integrating prior channel knowledge, the proposed method achieves significantly lower normalized mean square error compared to existing approaches while simultaneously reducing computational complexity.

Technology Category

• Machine Learning: Bayesian Learning
• Search and Optimization: Learning to Search
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• OTFS modulation
• channel estimation
• structured sparsity
• clustering characteristics
• CDL channels

Innovation

• Nonparametric Bayesian Learning
• OTFS Modulation
• Stick-breaking Process
• Clustered Delay Line
• Channel Estimation