Regional Resource Management for Service Provisioning in LEO Satellite Networks: A Topology Feature-Based DRL Approach

TL;DR

This paper introduces a topology feature-based deep reinforcement learning algorithm for regional resource management in LEO satellite networks, enabling adaptive, scalable, and efficient E2E service provisioning amid topology dynamics.

Contribution

It proposes a novel RRM mode and a DRL algorithm that adapt to network scale changes, improving service performance and convergence speed.

Findings

Achieves over 2.7% to 11.9% performance gains compared to existing algorithms.

Demonstrates rapid convergence and adaptability to network scale variations.

Effectively manages resource chains for end-to-end service provisioning.

Abstract

Satellite networks with wide coverage are considered natural extensions to terrestrial networks for their long-distance end-to-end (E2E) service provisioning. However, the inherent topology dynamics of low earth orbit satellite networks and the uncertain network scales bring an inevitable requirement that resource chains for E2E service provisioning must be efficiently re-planned. Therefore, achieving highly adaptive resource management is of great significance in practical deployment applications. This paper first designs a regional resource management (RRM) mode and further formulates the RRM problem that can provide a unified decision space independent of the network scale. Subsequently, leveraging the RRM mode and deep reinforcement learning framework, we develop a topology feature-based dynamic and adaptive resource management algorithm to combat the varying network scales. The proposed algorithm successfully takes into account the fixed output dimension of the neural network and the changing resource chains for E2E service provisioning. The matched design of the service orientation information and phased reward function effectively improves the service performance of the algorithm under the RRM mode. The numerical results demonstrate that the proposed algorithm with the best convergence performance and fastest convergence rate significantly improves service performance for varying network scales, with gains over compared algorithms of more than 2.7%, 11.9%, and 10.2%, respectively.