BEM Based Channel Estimation via Sparse Bayesian Learning for OTFS over Fast Time-Varying Channel

This paper proposes a sparse Bayesian learning (SBL)-based channel estimation algorithm for orthogonal time frequency space (OTFS) systems. By introducing the basis expansion model (BEM), we reconstruct the original high-dimensional time-varying channel matrix to a low-dimensional weighted combinati...

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Bibliographic Details
Published in2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring) pp. 01 - 05
Main Authors Zhang, Xing, Chen, Fangjiong, Feng, Jie, Zhou, Maowu, Yu, Hua
Format Conference Proceeding
LanguageEnglish
Published IEEE 24.06.2024
Subjects
Accuracy
basis expansion model (BEM)
Channel estimation
Estimation
Orthogonal time-frequency space (OTFS)
Simulation
sparse Bayesian learning
Symbols
Time-frequency analysis
Vehicular and wireless technologies
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Summary:This paper proposes a sparse Bayesian learning (SBL)-based channel estimation algorithm for orthogonal time frequency space (OTFS) systems. By introducing the basis expansion model (BEM), we reconstruct the original high-dimensional time-varying channel matrix to a low-dimensional weighted combination of basis functions. Then, we propose a two-stage SBL model that estimates the coefficients of the basis functions by leveraging the sparseness of the channel in the delay-Doppler (DD) domain. Further, an iterative decision feedback scheme is proposed to refine the accuracy of channel estimation and symbol detection. Simulation results show that even with a few iterations, the accuracy of channel estimation can be significantly improved. Besides, the proposed data-aided two-stage SBL-based OTFS channel estimation algorithm outperforms the existing OTFS receivers exactly in terms of estimation mean square error and bit error rate.
ISSN:2577-2465
DOI:10.1109/VTC2024-Spring62846.2024.10683205