Deep Reinforcement Learning-Based QoS-Aware Routing Protocol for Space–Air–Ground Integrated Networks
TL;DR
A deep reinforcement learning (DRL)-based adaptive routing scheme for maximizing throughput and minimizing end-to-end delay jointly in SAGIN and indicates that adaptive policy learning enables better congestion avoidance and more efficient resource utilization.
Abstract
Space–Air–Ground Integrated Networks (SAGINs) have been envisioned to support next-generation communication networks.Due to their heterogeneity and dynamic link characteristics, routing in SAGIN is challenging. Existing shortest-path routing mechanisms do not adapt well to the varying bandwidth and latency, leading to poor quality of service (QoS). In this paper, we propose a deep reinforcement learning (DRL)-based adaptive routing scheme for maximizing throughput and minimizing end-to-end delay jointly in SAGIN. In the proposed model, an agent learns the policy of choosing the suitable path by interacting with the network environment and obtaining rewards. The network is modeled as a weighted graph with delay and bandwidth constraints. We compare our model with a traditional delay minimization baseline over multiple independent runs. Experimental results show that our DRL approach achieves a 6.51% improvement in average throughput and a 29.90% reduction in end-to-end delay compared to the baseline strategy. Statistical analysis confirms the robustness of the delay reduction, highlighting the effectiveness of reinforcement learning in dynamic HetNets. This indicates that adaptive policy learning enables better congestion avoidance and more efficient resource utilization. Overall, the proposed DRL-based routing framework offers a scalable and intelligent solution for optimizing performance in complex SAGIN architectures, with promising potential for next generation integrated communication systems.