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

Tengfei Qi, Yifei Yang, Xiong Deng, Zhinan Sun, Ziqiang Gao, Xihua Zou, Wei Pan, Lianshan Yan

TL;DR

The paper tackles robust channel estimation for OTFS in the delay-Doppler domain under high Doppler, where relaxed sparse Bayesian learning (SBL) methods can generate pseudo peaks at low SNR. It introduces an adaptive Bayesian-threshold mechanism embedded within inverse-free SBL (IFSBL) to form the IFSBL-T algorithm, enabling active denoising and sparsity preservation with low computational burden via EM updates. The key contributions are the integration of Bayesian-thresholding into the IFSBL framework, derivation of the EM and threshold-update steps, and demonstration that IFSBL-T achieves notable NMSE gains (≈2.6 dB over IFSBL and ≈0.6 dB over SBL) with faster convergence. These results imply improved OTFS channel estimation performance in high-mobility environments without increasing complexity, benefiting reliable communications in next-generation wireless systems.

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.

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

TL;DR

The paper tackles robust channel estimation for OTFS in the delay-Doppler domain under high Doppler, where relaxed sparse Bayesian learning (SBL) methods can generate pseudo peaks at low SNR. It introduces an adaptive Bayesian-threshold mechanism embedded within inverse-free SBL (IFSBL) to form the IFSBL-T algorithm, enabling active denoising and sparsity preservation with low computational burden via EM updates. The key contributions are the integration of Bayesian-thresholding into the IFSBL framework, derivation of the EM and threshold-update steps, and demonstration that IFSBL-T achieves notable NMSE gains (≈2.6 dB over IFSBL and ≈0.6 dB over SBL) with faster convergence. These results imply improved OTFS channel estimation performance in high-mobility environments without increasing complexity, benefiting reliable communications in next-generation wireless systems.

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.

Paper Structure

This paper contains 11 sections, 1 theorem, 24 equations, 5 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. OTFS System Model
  3. OTFS Input and Output Model
  4. OTFS Channel Estimation Model
  5. Channel Estimation Based On Embedded Bayesian Threshold
  6. Inverse-free SBL for OTFS Channel Estimation
  7. Expectation-Maximization Algorithm
  8. Embedded Bayesian Threshold
  9. Proposed Algorithm
  10. Simulation Results
  11. Conclusion

Key Result

Lemma 1

The Bayesian threshold for variance under the hypothesis when the SBL algorithm converges, can be written as the following iterative expression where $\rho_n^{(t)} = \ln\frac{{P({H_1}|{\alpha _n^{(t)}})}}{{P({H_0}|{\alpha _n^{(t)}})}}$ is the result of the $t$-th iteration.

Figures (5)

  • Figure 1: The arrangement of pilot and data symbols in the DD domain.
  • Figure 2: NMSE of OTFS channel estimation under different algorithms.
  • Figure 3: BER of OTFS under different algorithms.
  • Figure 4: Comparison of convergence speed and performance among different algorithms.
  • Figure 5: Success rates of respective algorithms versus Number of Measurement.

Theorems & Definitions (2)

  • Lemma 1
  • proof