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

2025-12-08

AI Summary

For high-Doppler Orthogonal Time Frequency Space (OTFS) systems operating at low signal-to-noise ratios (SNRs), existing channel estimation methods suffer from severe spurious peaks, poor noise resilience, and high computational complexity. To address these issues, this paper formulates the delay-Doppler domain channel estimation as a sparse signal recovery problem and proposes an adaptive-threshold channel estimation algorithm based on Sparse Bayesian Learning (SBL). Its key innovations include: (i) a data-driven active denoising mechanism that dynamically suppresses spurious peaks via adaptive Bayesian thresholding—avoiding performance degradation inherent in conventional hard thresholding; and (ii) integration of inverse-free SBL to drastically reduce matrix inversion complexity. Simulation results demonstrate that, at comparable computational complexity, the proposed method achieves 3–5 dB lower mean square error (MSE) than state-of-the-art approaches, while suppressing over 90% of spurious peaks—achieving an optimal trade-off among estimation accuracy, robustness, and computational efficiency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

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.

Problem

• Enhances channel estimation for OTFS systems
• Addresses pseudo-peak issues in low SNR scenarios
• Reduces algorithm complexity while improving anti-noise performance

Innovation

• Adaptive Bayesian threshold denoising mechanism
• Inverse-free sparse Bayesian learning framework
• Sparse signal recovery for OTFS channel estimation