Temporal Correlation and Message-Passing-Based Sparse Bayesian Learning Channel Estimation for Underwater Acoustic Communications

Yin, Jingwei; Zhu, Guangjun; Han, Xiao; Guo, Longxiang; Li, Lin; Ge, Wei

doi:10.1109/joe.2023.3330523

Cited by 5 publications

(1 citation statement)

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“…Currently, minimal research has focused on time-varying channel models and their channel estimation and equalization. In [30], we proposed a TC-AMP algorithm for time-varying channels; it uses a two-layer iterative algorithm to improve the performance of channel equalization. In this paper, we further employ the superimposed training sequence strategy and GMM-distribution-based assumptions.…”

Section: Introductionmentioning

confidence: 99%

Iterative Signal Detection and Channel Estimation with Superimposed Training Sequences for Underwater Acoustic Information Transmission in Time-Varying Environments

Li,

Han,

2024

Remote Sensing

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Underwater signal processing is primarily based on sound waves because of the unique properties of water. However, the slow speed and limited bandwidth of sound introduce numerous challenges, including pronounced time-varying characteristics and significant multipath effects. This paper explores a channel estimation method utilizing superimposed training sequences. Compared with conventional schemes, this method offers higher spectral efficiency and better adaptability to time-varying channels owing to its temporal traversal. To ensure success in this scheme, it is crucial to obtain time-varying channel estimation and data detection at low SNRs given that superimposed training sequences consume power resources. To achieve this goal, we initially employ coarse channel estimation utilizing superimposed training sequences. Subsequently, we employ approximate message passing algorithms based on the estimated channels for data detection, followed by iterative channel estimation and equalization based on estimated symbols. We devise an approximate message passing channel estimation method grounded on a Gaussian mixture model and refine its hyperparameters through the expectation maximization algorithm. Then, we refine the channel information based on time correlation by employing an autoregressive hidden Markov model. Lastly, we perform numerical simulations of communication systems by utilizing a time-varying channel toolbox to simulate time-varying channels, and we validate the feasibility of the proposed communication system using experimental field data.

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Section: Introductionmentioning

confidence: 99%