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Variational Bayesian Sparse Decision-Feedback Equalization Based on RLS Algorithm in Underwater Acoustic Communications
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DOI：10.1109/LCOMM.2025.3649767.png)
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摘要
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This letter proposes a variational Bayesian sparse decision-feedback equalizer (VB-SDFE) based on the recursive least squares (RLS) algorithm for underwater acoustic communications. The method employs Bayesian inference to optimize the sparse pre-equalizer coefficients in overlapping subblocks and addresses the sensitivity of RLS-based decision-feedback equalizer (RLS-DFE) to channel variations. Key features include subblock processing with a forgetting factor, variational updates for sparsity control, and Doppler-resistant phase compensation. Simulations show that the proposed algorithm achieves superior and robust performance under different channel conditions. Sea trials demonstrate a 7.8-dB gain in SNR and error-free performance at 21.5 km.
Keyword:
Symbols
Decision feedback equalizers
Bayes methods
Convergence
Transversal filters
Oceans
Indexes
Computational complexity
Adaptive filters
Underwater acoustics
Underwater acoustic (UWA) communications
variational Bayesian
sparse
decision-feedback equalization (DFE)
recursive least squares (RLS)
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
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