Joint Spectrum Sensing and Resource Allocation for OFDMA-based Underwater Acoustic Communications

TL;DR

This paper introduces a deep reinforcement learning approach to optimize spectrum sensing and resource allocation in OFDMA-based underwater acoustic cognitive radio networks, significantly improving spectral efficiency and communication success.

Contribution

It presents a novel end-to-end DRL-based method for joint spectrum sensing and resource allocation tailored for underwater OFDMA cognitive radio systems, addressing synchronization challenges.

Findings

Outperforms baseline schemes by 42.9% in spectral efficiency

Achieves 4.4% higher communication success rate

Demonstrates robustness in dynamic underwater environments

Abstract

Underwater acoustic (UWA) communications generally rely on cognitive radio (CR)-based ad-hoc networks due to challenges such as long propagation delay, limited channel resources, and high attenuation. To address the constraints of limited frequency resources, UWA communications have recently incorporated orthogonal frequency division multiple access (OFDMA), significantly enhancing spectral efficiency (SE) through multiplexing gains. Still, {the} low propagation speed of UWA signals, combined with {the} dynamic underwater environment, creates asynchrony in multiple access scenarios. This causes inaccurate spectrum sensing as inter-carrier interference (ICI) increases, which leads to difficulties in resource allocation. As efficient resource allocation is essential for achieving high-quality communication in OFDMA-based CR networks, these challenges degrade communication reliability in UWA systems. To resolve the issue, we propose an end-to-end sensing and resource optimization method using deep reinforcement learning (DRL) in an OFDMA-based UWA-CR network. Through extensive simulations, we confirm that the proposed method is superior to baseline schemes, outperforming other methods by 42.9 % in SE and 4.4 % in communication success rate.