Channel Estimation with Hierarchical Sparse Bayesian Learning for ODDM Systems

2026-02-08

AI Summary

This work addresses the challenge in existing orthogonal time frequency space (OTFS)-based delay-Doppler domain channel estimation systems, where strong coupling between delay and Doppler parameters hinders the simultaneous achievement of high accuracy and low computational complexity. To overcome this, the paper proposes a two-dimensional hierarchical sparse Bayesian learning (HSBL) framework that leverages a partially decoupled sparse signal model. The approach first performs a low-complexity coarse estimation in the virtual delay–Doppler domain and subsequently refines the estimate by constructing a high-resolution fine grid within critical regions to enable off-grid precision. By innovatively integrating partial decoupling with a hierarchical estimation strategy, the method effectively mitigates parameter coupling, significantly reduces computational complexity, and achieves superior channel estimation performance compared to conventional off-grid two-dimensional sparse Bayesian learning approaches.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation
• Machine Learning: Bayesian Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Orthogonal delay-Doppler division multiplexing (ODDM) is a promising modulation technique for reliable communications in high-mobility scenarios. However, the existing channel estimation frameworks for ODDM systems cannot achieve both high accuracy and low complexity simultaneously, due to the inherent coupling of delay and Doppler parameters. To address this problem, a two-dimensional (2D) hierarchical sparse Bayesian learning (HSBL) based channel estimation framework is proposed in this paper. Specifically, we address the inherent coupling between delay and Doppler dimensions in ODDM by developing a partially-decoupled 2D sparse signal recovery (SSR) formulation on a virtual sampling grid defined in the delay-Doppler (DD) domain. With the help of the partially-decoupled formulation, the proposed 2D HSBL framework first performs low-complexity coarse on-grid 2D sparse Bayesian learning (SBL) estimation to identify potential channel paths. Then, high-resolution fine grids are constructed around these regions, where an off-grid 2D SBL estimation is applied to achieve accurate channel estimation. Simulation results demonstrate that the proposed framework achieves performance superior to conventional off-grid 2D SBL with significantly reduced computational complexity.

Problem

• channel estimation
• ODDM
• delay-Doppler coupling
• sparse signal recovery
• high-mobility communications

Innovation

• Hierarchical Sparse Bayesian Learning
• ODDM
• Delay-Doppler Domain
• Partially-Decoupled Sparse Recovery
• Off-Grid Channel Estimation