Underwater Acoustic Communication Using Multi-Layer Vector Approximate Message Passing Evaluated on the Watermark Benchmark

The multi-layer vector approximate message passing algorithm is applied to the problem of channel estimation in underwater acoustic communication addressing the estimation of time-varying channel characteristics. A pre-trained artificial neural network is used as prior for the channel estimation. The communication receiver is designed as a factor graph with a low-density parity check decoder as factor node. The performance of the proposed communication scheme is evaluated by comparing the bit error rates with classical communication schemes. The bit error rate is significantly decreased on strongly time-varying channels. Different datasets are compared for training the channel estimator.