Time-Reversal CNN-Based S-NOFDM Scheme for Underwater Acoustic Communication

Multicarrier modulation shows the advantages of improving bandwidth efficiency in underwater acoustic (UWA) communications, whereas the more spectrum-efficient nonorthogonal frequency division multiplexing (NOFDM) is seldom investigated in the underwater scenario. The main idea of the NOFDM is to use overlapped subcarriers for increasing spectral efficiency. However, the NOFDM suffers from severe distortions both in the time and frequency domains because of the UWA channel's multipath propagation and Doppler spread. Considering these problems, we propose a novel time-reversal convolutional neural network (TR-CNN) based sparse NOFDM (S-NOFDM) scheme for UWA communication. The S-NOFDM waveform is determined by solving the sparse representation of input symbols under a set of nonorthogonal subcarriers, which can allocate symbols sparser to subcarriers and reduce distortion caused by multipath propagation and Doppler spread. The proposed TR-CNN is constructed with a time-reversal (T-R) convolutional layer where the T-R process is used to deal with multipath propagation. After the received signal has passed through the T-R layer, the main path of the UWA channel strength is enhanced and the other paths are weakened. The TR-CNN is also used to learn the inherent information between S-NOFDM symbols, improving UWA channel adaptability. After the training, the proposed scheme can be applied in the real-time UWA communication implementation. The simulation and experimental results prove that the proposed scheme outperforms the traditional scheme in signal detection and recovery. It is also robust with general capabilities in various UWA channel environments.

Automated summary

This article proposes a novel sparse NOFDM scheme based on TR-CNN for underwater communication. The scheme reduces distortion caused by multipath propagation and Doppler spread by using nonorthogonal subcarriers and improves the adaptability of the underwater acoustic channel using TR-CNN. Experimental results demonstrate that the proposed scheme outperforms the traditional scheme in signal detection and recovery, and exhibits robustness in different underwater acoustic channel environments.