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.

本研究提出了一种新颖的基于强化学习的交接协议（DHO），针对低地球轨道（LEO）卫星网络中长延迟挑战进行设计。DHO通过预定的LEO卫星轨道图案进行训练，利用其预测能力跳过交接过程中的测量报告（MR），从而消除MR阶段产生的传播延迟，并提供有效的交接决策。经实验证明，DHO在各种网络条件下优于传统交接协议，表现出对实际网络的适用性。此外，该研究还探讨了访问延迟与冲突率之间的权衡，以及使用不同DRL算法评估DHO的训练性能和收敛性。

低轨卫星网络的切换协议学习：接入延迟和冲突最小化