Enhancing satellite networks with deep reinforcement learning: A focus on IoT connectivity and dynamic resource management

Abstract

The integration of satellite-based networks into our communication infrastructure is increasingly becoming standard practice, driven by the widespread adoption of 5G technologies and the escalating demand for continuous content delivery. Achieving this integration efficiently necessitates significant improvements in reducing latency and enhancing data throughput. In response to these challenges, we propose the development of a specialized satellite architecture alongside a novel algorithm focused on call access and admission control within Low Earth Orbit (LEO) satellite systems. This solution leverages an Artificial Intelligence (AI) driven approach, employing a Deep Reinforcement Learning (DRL) agent to automate satellite operations through advanced beam localization techniques. Furthermore, we introduce a beam protocol that seamlessly integrates this automated learning mechanism into its operational framework. The efficacy of our proposed algorithm is rigorously evaluated through simulation studies of a multibeam satellite system, demonstrating the potential of Deep Reinforcement Learning in facilitating dynamic resource allocation with improved efficiency.

Keywords

• Satellite network
• Software defined network
• Network virtualization
• 5G
• AI