Joint Message-Passing-Based Bidirectional Channel Estimation and Equalization With Superimposed Training for Underwater Acoustic Communications

Guang Yang, Qinghua Guo, Hanxue Ding, Qi Yan, Defeng David Huang

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

Abstract

Acquiring accurate channel state information and mitigating severe intersymbol interference are challenging for underwater acoustic communications with moving transceivers due to the rapid changes of the underwater acoustic channels. In this work, we address the issue using a superimposed training (ST) scheme with a powerful channel estimation method. Different from the conventional time-multiplexed training, training sequences with a small power are superimposed with symbol sequences. The training signals are transmitted over all time, leading to enhanced tracking capability to deal with time-varying channels at the cost of only a small power loss. To realize this, based on the belief propagation, we develop a message-passing-based bidirectional channel estimation (BCE) algorithm, where all messages are Gaussian, enabling efficient implementation. In particular, the channel correlations are fully exploited through a forward recursion and a backward recursion, thereby achieving accurate channel estimation. Moreover, the ST-based BCE is combined with channel equalization (in the frequency domain) and decoding, and they are performed jointly in an iterative manner to significantly enhance the overall system performance. Field experiments were carried out in Jiaozhou Bay in 2019, and the results verify the effectiveness of the proposed scheme and algorithm.

Original languageEnglish
Pages (from-to)1463-1476
Number of pages14
JournalIEEE Journal of Oceanic Engineering
Volume46
Issue number4
Early online date23 Mar 2021
DOIs
Publication statusPublished - 1 Oct 2021

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