Stable Acoustic Relay Assignment with High Throughput via Lase Chaos-based Reinforcement Learning

TL;DR

This paper introduces a laser chaos-based reinforcement learning method for stable and high-throughput acoustic relay assignment in underwater networks, effectively handling environmental variability and ambiguous stability conditions.

Contribution

It proposes a novel laser chaos-based multi-processing learning approach for relay assignment, enhancing stability and throughput in complex underwater acoustic networks.

Findings

Laser chaos improves random number generation for learning.

The method achieves higher throughput and adaptability.

Ambiguous stable arrangements reduce volatility.

Abstract

This study addresses the problem of stable acoustic relay assignment in an underwater acoustic network. Unlike the objectives of most existing literature, two distinct objectives, namely classical stable arrangement and ambiguous stable arrangement, are considered. To achieve these stable arrangements, a laser chaos-based multi-processing learning (LC-ML) method is introduced to efficiently obtain high throughput and rapidly attain stability. In order to sufficiently explore the relay's decision-making, this method uses random numbers generated by laser chaos to learn the assignment of relays to multiple source nodes. This study finds that the laser chaos-based random number and multi-processing in the exchange process have a positive effect on higher throughput and strong adaptability with environmental changing over time. Meanwhile, ambiguous cognitions result in the stable configuration with less volatility compared to accurate ones. This provides a practical and useful method and can be the basis for relay selection in complex underwater environments.