Deep-Learning Based Ship-Radiated Noise Suppression for Underwater Acoustic OFDM Systems

Interference due to ship-radiated noise in the underwater acoustic (UA) channel generates additive distortions that degrade wireless UA communications signals. Compressed sensing (CS) techniques are an approach used to estimate and suppress the impulsive components of ship-radiated noise for orthogonal frequency-division multiplexing (OFDM) systems by exploiting the null sub-carriers not used for data transmission. However, these CS-based estimation methods are constrained to estimating sparse signals and typically require slow iterative solvers. To combat these drawbacks, we propose a deep learning (DL) approach to structured signal recovery for estimating and mitigating the interfering effects of ship-radiated noise for OFDM systems. Our results indicate that the DL models, trained via publicly available long term acoustic data of shipping noise signals, produce measurable mitigation gains to the benchmark CS algorithms. In addition, we show the DL models outperform the benchmark CS estimation methods on new never before 'seen' experimentally acquired ship-radiated noise data.