The Next Generation Heterogeneous Satellite Communication Networks: Integration of Resource Management and Deep Reinforcement Learning

Abstract

This article proposes an innovative resource management framework for the next generation heterogeneous satellite networks (HSNs), which can achieve cooperation between independent satellite systems and maximizing resource utilization. The key points of the proposed design lie in the architecture that supports the intercommunication between different satellite systems, and the SDN/NFV-based management offering the matching between resources and services. Based on the framework, we then apply deep reinforcement learning (DRL) into the system due to its strong ability in optimal matching. The two problems of multiobjective reinforcement learning and multiagent reinforcement learning are studied to adapt the development of the HSN. The combination of the DRL and resource allocation achieves integrated resource management across different satellite systems and achieves resource allocation in the HSN which can be implemented more flexibly and efficiently.