QoS-Guarantee Resource Allocation of Slicing Services in Integrated Satellite-Terrestrial Networks Based on Deep Reinforcement Learning

Integrated satellite-terrestrial networks (ISTNs) enable global connectivity but face challenges in efficient resource allocation due to increasing service demands. To address Quality of Service (QoS) degradation caused by inefficient resource allocation in ISTN's heterogeneous network, we propose a network slicing (NS) resource allocation algorithm based on deep reinforcement learning (DRL). First, an ISTN system model is constructed using NS, along with an evaluation approach for slicing services. Next, a satisfaction utility function is defined to quantify the QoS of slicing services, and an optimization problem is formulated. Then, based on the Markov decision process (MDP) and dueling double deep Q-learning (D3QN) theory, an NS resource allocation algorithm is designed, comprising both training and execution phases. Simulation results demonstrate that the proposed algorithm outperforms baseline approaches in system satisfaction, bandwidth allocation, and satellite network utilization.