Handover Protocol Learning for LEO Satellite Networks: Access Delay and
  Collision Minimization
Ju-Hyung Lee Chanyoung Park Soohyun Park Andreas F. Molisch
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.