Optimizing Handover Decisions in Multi-Connectivity Enabled Terrestrial-Satellite Integrated Networks: A Deep Reinforcement Learning Approach

The integration of 5G terrestrial networks with Low Earth Orbit (LEO) satellites has the potential to provide seamless global connectivity and enhanced service quality, particularly in regions with limited terrestrial infrastructure such as rural areas. Furthermore, the incorporation of Multi-connectivity (MC) enables user equipment (UEs) to maintain simultaneous connections with both terrestrial 5G base stations and LEO satellites, improving system reliability. However, the high mobility of LEO satellites and the dynamic behavior of UEs present significant challenges, particularly in handover decision-making which can adversely impact system throughput, and quality of service (QoS). To address these challenges, we propose a novel deep reinforcement learning-based approach that integrates online Random Ensemble Mixture and Dual Experience Replay into a Dueling Double Deep Q-Network architecture. This proposed scheme intelligently optimizes handover decisions in MC-enabled terrestrial-satellite networks, improving decision accuracy in highly dynamic scenarios. Simulation results demonstrate substantial gains in system throughput, reduced system delay, average handover reduction, and increased transmission success probability, setting new performance benchmarks for integrated terrestrial-satellite networks while adhering to diverse QoS requirements.