An Efficient and Adaptive Framework for Achieving Underwater High-performance Maintenance Networks

TL;DR

This paper introduces U-HPNF, an adaptive hierarchical framework utilizing deep reinforcement learning, federated learning, and digital twins to enhance the performance and self-management of underwater communication networks within integrated space-air-ground-aqua systems.

Contribution

It proposes a novel AI-native hierarchical framework combining DRL, FL, and digital twins for efficient resource management and adaptability in underwater networks.

Findings

Effective optimization of network performance demonstrated

Framework adapts to varying QoS requirements

Reduces communication overhead and preserves privacy

Abstract

With the development of space-air-ground-aqua integrated networks (SAGAIN), high-speed and reliable network services are accessible at any time and any location. However, the long propagation delay and limited network capacity of underwater communication networks (UCN) negatively impact the service quality of SAGAIN. To address this issue, this paper presents U-HPNF, a hierarchical framework designed to achieve a high-performance network with self-management, self-configuration, and self-optimization capabilities. U-HPNF leverages the sensing and decision-making capabilities of deep reinforcement learning (DRL) to manage limited resources in UCNs, including communication bandwidth, computational resources, and energy supplies. Additionally, we incorporate federated learning (FL) to iteratively optimize the decision-making model, thereby reducing communication overhead and protecting the privacy of node observation information. By deploying digital twins (DT) at both the intelligent sink layer and aggregation layer, U-HPNF can mimic numerous network scenarios and adapt to varying network QoS requirements. Through a three-tier network design with two-levels DT, U-HPNF provides an AI-native high-performance underwater network. Numerical results demonstrate that the proposed U-HPNF framework can effectively optimize network performance across various situations and adapt to changing QoS requirements.