Reinforcement-Learning-Enabled Beam Alignment for Water-Air Direct Optical Wireless Communications

TL;DR

This paper introduces a deep reinforcement learning approach for beam alignment in water-air optical wireless communications, addressing dynamic interface challenges to improve signal quality in underwater-to-air data transmission.

Contribution

It develops a novel DRL-based beam alignment strategy using DDPG and a specialized reward function for water-air optical links, enhancing transmission reliability.

Findings

DRL-based scheme outperforms traditional methods in simulations.

The proposed reward function improves alignment accuracy.

Simulation validates the effectiveness of the approach.

Abstract

The escalating interests on underwater exploration/reconnaissance applications have motivated high-rate data transmission from underwater to airborne relaying platforms, especially under high-sea scenarios. Thanks to its broad bandwidth and superior confidentiality, Optical wireless communication has become one promising candidate for water-air transmission. However, the optical signals inevitably suffer from deviations when crossing the highly-dynamic water-air interfaces in the absence of relaying ships/buoys. To address the issue, this article proposes one novel beam alignment strategy based on deep reinforcement learning (DRL) for water-air direct optical wireless communications. Specifically, the dynamic water-air interface is mathematically modeled using sea-wave spectrum analysis, followed by characterization of the propagation channel with ray-tracing techniques. Then the deep deterministic policy gradient (DDPG) scheme is introduced for DRL-based transceiving beam alignment. A logarithm-exponential (LE) nonlinear reward function with respect to the received signal strength is designed for high-resolution rewarding between different actions. Simulation results validate the superiority of the proposed DRL-based beam alignment scheme.