Deep learning aided underwater acoustic OFDM receivers: Model-driven or data-driven?

Abstract

The Underwater Acoustic (UWA) channel is bandwidth-constrained and experiences doubly selective fading. It is challenging to acquire perfect channel knowledge for Orthogonal Frequency Division Multiplexing (OFDM) communications using a finite number of pilots. On the other hand, Deep Learning (DL) approaches have been very successful in wireless OFDM communications. However, whether they will work underwater is still a mystery. For the first time, this paper compares two categories of DL-based UWA OFDM receivers: the Data-Driven (DD) method, which performs as an end-to-end black box, and the Model-Driven (MD) method, also known as the model-based data-driven method, which combines DL and expert OFDM receiver knowledge. The encoder-decoder framework and Convolutional Neural Network (CNN) structure are employed to establish the DD receiver. On the other hand, an unfolding-based Minimum Mean Square Error (MMSE) structure is adopted for the MD receiver. We analyze the characteristics of different receivers by Monte Carlo simulations under diverse communications conditions and propose a strategy for selecting a proper receiver under different communication scenarios. Field trials in the pool and sea are also conducted to verify the feasibility and advantages of the DL receivers. It is observed that DL receivers perform better than conventional receivers in terms of bit error rate.