Variance State Propagation for Channel Estimation in Underwater Acoustic Massive MIMO-OFDM with Clustered Channel Sparsity

In this paper, we investigate the channel estimation problem in underwater acoustic (UWA) massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We first present a parametric UWA massive MIMO-OFDM channel model, and then formulate the channel estimation task as a sparse signal recovery problem. By exploiting the structured sparsity in the delay-Doppler-angle domain of the time-varying massive MIMO-OFDM channel, we develop a message-passing based channel estimation algorithm under the variance state propagation (VSP) framework. Simulation results show that our approach attains a significant performance improvement over the existing methods.