Handover Protocol Learning for LEO Satellite Networks: Access Delay and Collision Minimization

TL;DR

This paper introduces a deep reinforcement learning-based handover protocol for LEO satellite networks that reduces access delay and collisions by predicting satellite positions, eliminating the need for measurement reports.

Contribution

The study develops DHO, a novel DRL-based handover protocol that skips measurement reports, significantly reducing delays and collisions in LEO satellite networks.

Findings

DHO reduces access delay compared to legacy protocols.

DHO lowers collision rates in satellite handovers.

DHO demonstrates effective convergence with various DRL algorithms.

Abstract

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.