Model-Driven Based Deep Unfolding Equalizer for Underwater Acoustic OFDM Communications

TL;DR

This paper introduces UDNet, a deep unfolding equalizer for underwater acoustic OFDM systems, which outperforms traditional MMSE equalizers in symbol error rate while maintaining low computational complexity.

Contribution

The paper presents a novel deep unfolding equalizer tailored for underwater acoustic OFDM, incorporating a sliding structure and classification-based QPSK equalization.

Findings

UDNet achieves nearly ten times lower SER than MMSE.

UDNet maintains performance across different signal lengths.

The approach reduces computational complexity compared to traditional methods.

Abstract

It is challenging to design an equalizer for the complex time-frequency doubly-selective channel. In this paper, we employ the deep unfolding approach to establish an equalizer for the underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system, namely UDNet. Each layer of UDNet is designed according to the classical minimum mean square error (MMSE) equalizer. Moreover, we consider the QPSK equalization as a four-classification task and adopt minimum Kullback-Leibler (KL) to achieve a smaller symbol error rate (SER) with the one-hot coding instead of the MMSE criterion. In addition, we introduce a sliding structure based on the banded approximation of the channel matrix to reduce the network size and aid UDNet to perform well for different-length signals without changing the network structure. Furthermore, we apply the measured at-sea doubly-selective UWA channel and offshore background noise to evaluate the proposed equalizer. Experimental results show that the proposed UDNet performs better with low computational complexity. Concretely, the SER of UDNet is nearly an order of magnitude lower than that of MMSE.