Joint Channel Estimation and Decoding for Underwater Acoustic Communication With a Short Pilot Sequence

Abstract

With the advance of undersea communication technologies, the operation of large numbers of small submerged devices and their connection, such as the Internet of Underwater Things (IoUT), is becoming possible. Deployed over long periods of time, energy restrictions limit the application of underwater acoustic (UWA) communication, and thus, the peer-to-peer (P2P) communication packets should be small with minimum overhead dedicated to training symbols. On the other hand, the UWA communication in physical layer requires good equalization performance to manage the inevitably long intersymbol interference. In this article, we propose a semiblind joint channel estimation and decoding (JCED) approach for P2P UWA communication to allow efficient channel equalization despite the short training sequence. We formulate the JCED problem and solve it by passing messages between the raw acoustic observations, the nodes that characterize the connections of the channel's taps, and the information-bearing symbols within a turbo equalization framework. To initialize the prior of the channel's components, we employ the expectation maximization algorithm, assuming a Gaussian mixture model for the channel's taps. Extensive numerical simulation and results from a field experiment show that our approach outperforms state-of-the-art benchmarks in terms of the communication system's capability of performing well with a small number of pilot symbols.