Joint CFO, Gridless Channel Estimation and Data Detection for Underwater Acoustic OFDM Systems
Lei Wan, Jiang Zhu, En Cheng, Zhiwei Xu

TL;DR
This paper introduces an iterative, gridless Bayesian receiver for underwater acoustic OFDM systems that jointly estimates CFO, channel, and data with high resolution, outperforming existing grid-based methods.
Contribution
The paper presents a novel joint CFO, channel, and data estimation method using gridless variational Bayesian line spectra estimation, improving accuracy and reducing complexity.
Findings
Significantly better performance than grid-based GAMP methods.
Effective joint estimation of CFO, channel, and data.
Validated with numerical simulations and sea test data.
Abstract
In this paper, we propose an iterative receiver based on gridless variational Bayesian line spectra estimation (VALSE) named JCCD-VALSE that \emph{j}ointly estimates the \emph{c}arrier frequency offset (CFO), the \emph{c}hannel with high resolution and carries out \emph{d}ata decoding. Based on a modularized point of view and motivated by the high resolution and low complexity gridless VALSE algorithm, three modules named the VALSE module, the minimum mean squared error (MMSE) module and the decoder module are built. Soft information is exchanged between the modules to progressively improve the channel estimation and data decoding accuracy. Since the delays of multipaths of the channel are treated as continuous parameters, instead of on a grid, the leakage effect is avoided. Besides, the proposed approach is a more complete Bayesian approach as all the nuisance parameters such as the…
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Taxonomy
TopicsUnderwater Vehicles and Communication Systems · Underwater Acoustics Research · Blind Source Separation Techniques
