A Multi-Task Learning Framework for Underwater Acoustic Channel Prediction: Performance Analysis on Real-World Data

Abstract

In the rapidly advancing field of Underwater Acoustic Communication (UAC), channel prediction remains a major challenge, exacerbated by the complicated nature of ocean environments. This paper introduces an innovative Multi- Task Learning (MTL) framework for time-varying Underwater Acoustic (UWA) channel prediction. By decomposing the highdimensional Channel Impulse Response (CIR) prediction into interconnected tasks, the proposed framework leverages a Shared Feature Learning (SFL) layer, capturing intricate dependencies underlying UWA channels. To validate its efficacy, we conducted thorough evaluations, leveraging real-world data from two distinct at-sea experiments conducted in Wuyuan Bay, China. A comprehensive comparative study of various configurations for the SFL layer, ranging from commonly used Recurrent Neural Network (RNN)-based models to the more advanced transformer structure, further underscores the flexibility and broad applicability of our MTL framework for handling various challenging UWA environments.