Handover Protocol Learning for LEO Satellite Networks: Access Delay and Collision Minimization
This addresses handover efficiency for LEO satellite networks, representing an incremental improvement with specific gains.
The study tackled the challenge of long propagation delays in handover procedures for low-Earth orbit satellite networks by proposing a deep reinforcement learning-based protocol called DHO, which skips the Measurement Report phase to reduce delays and outperforms legacy protocols in access delay, collision rate, and handover success rate.
This study presents a novel deep reinforcement learning (DRL)-based handover (HO) protocol, called DHO, specifically designed to address the persistent challenge of long propagation delays in low-Earth orbit (LEO) satellite networks' HO procedures. DHO skips the Measurement Report (MR) in the HO procedure by leveraging its predictive capabilities after being trained with a pre-determined LEO satellite orbital pattern. This simplification eliminates the propagation delay incurred during the MR phase, while still providing effective HO decisions. The proposed DHO outperforms the legacy HO protocol across diverse network conditions in terms of access delay, collision rate, and handover success rate, demonstrating the practical applicability of DHO in real-world networks. Furthermore, the study examines the trade-off between access delay and collision rate and also evaluates the training performance and convergence of DHO using various DRL algorithms.