An Optimized CNN-Attention Network for Clipped OFDM Receiver of Underwater Acoustic Communications

Abstract

In underwater acoustic communication systems based on orthogonal frequency division multiplexing (OFDM), taking clipping to reduce the peak-to-average power ratio leads to nonlinear distortion of the signal, making the receiver unable to recover the faded signal accurately. In this letter, an Aquila optimizer-based convolutional attention block stacked network (AO-CABNet) is proposed to replace the receiver to improve the ability to recover the original signal. Simulation results show that the AO method has better optimization capability to quickly obtain the optimal parameters of the network model, and the proposed AO-CABNet structure outperforms existing schemes.