Vector-Approximate-Message-Passing-Based Channel Estimation for MIMO-OFDM Underwater Acoustic Communications

Abstract

Accurate channel estimation (CE) is critical to the performance of orthogonal frequency-division multiplexing (OFDM) underwater acoustic (UWA) communications, especially under multiple-input multiple-output (MIMO) scenarios. In this article, we explore vector approximate message passing (VAMP) coupled with expectation-maximization (EM) to obtain CE for MIMO OFDM UWA communications. The EM-VAMP-CE scheme is developed by employing a Bernoulli–Gaussian (BG) prior distribution for the channel impulse response, and hyperparameters of the BG prior distribution are learned via the EM algorithm. The performance of the EM-VAMP-CE is evaluated through both synthesized data and real data collected in two at-sea UWA communication experiments. It is shown that the EM-VAMP-CE achieves better performance–complexity tradeoff compared with the existing CE methods.