Intent-Based Network Resource Orchestration in Space-Air-Ground Integrated Networks: A Graph Neural Networks and Deep Reinforcement Learning Approach

The Space-Air-Ground Integrated Network (SAGIN) offers a promising solution for seamless connectivity, high data rates, and wide-area coverage. However, its multi-segment architecture poses significant challenges in efficient resource management and Quality of Service assurance across diverse services. To address these challenges, we propose an Intent-Based Networking (IBN) system that streamlines network automation within the SAGIN ecosystem. Our system model integrates an IBN module with the SAGIN infrastructure, allowing network operators to express their service intents, such as Low-Latency Virtual Network Requests (LLVNRs) and High-Bandwidth Virtual Network Requests (HBVNRs), along with their respective QoS requirements. To tackle the inherent complexity, we employ a Deep Deterministic Policy Gradient (DDPG) based DRL-IBN framework. The DRL agent interacts with the SAGIN environment using a feature matrix extracted via Graph Neural Network (GNN), facilitating informed decision-making for resource allocation based on VNR acceptance. Through extensive simulations and numerical evaluations, we demonstrate the superiority of our proposed DRL-IBN algorithm over baseline approaches, such as LC-VNE and RW-MM-SP, in maximizing system utility, ensuring QoS satisfaction, and enabling efficient resource utilization in the dynamic and heterogeneous SAGIN environment. Our results indicate that the DRL-IBN framework achieves higher VNR acceptance ratios, better resource utilization, and more effective QoS assurance based on minimum QoS violation, proving its effectiveness and robustness in managing the complexities of the SAGIN network.