A Novel DNN Based Channel Estimator for Underwater Acoustic Communications with IM-OFDM

Performance of acoustic communication system in shallow sea is influenced by complicated interferences. Multipath with large delays and strong reflections leads to serious transmission error. To support reliable and efficient transmission in the background above, in this paper, a deep learning based channel estimator for underwater index modulated orthogonal frequency division modulation (IM-OFDM) is proposed. A deep neural network is designed and trained with real channels tested in Xiamen sea area. The extracted real channels are collected and analyzed, constituting a mixed database with the channels generated using real parameters. Via a half-physical simulation, the performance is evaluated with different channel estimators. The results prove stability of the performance with the proposed channel estimator in different communication distances of shallow water. In conclusion, the deep learning based underwater IM-OFDM channel estimator obtains significant performance in target shallow sea scenarios, which is promising as a solution to the adaptive scheme in changing underwater environment.