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.

翻译：本研究提出了一种基于深度强化学习的切换协议DHO，专门用于解决低轨卫星网络切换过程中长期存在的传播时延问题。DHO利用低轨卫星的预定轨道模式训练后的预测能力，跳过了切换流程中的测量报告（MR）环节。这一简化过程消除了MR阶段产生的传播时延，同时仍能提供有效的切换决策。实验表明，在不同网络条件下，所提出的DHO协议在接入时延、碰撞率和切换成功率方面均优于传统切换协议，验证了DHO在实际网络中的适用性。此外，本研究还探讨了接入时延与碰撞率之间的权衡关系，并评估了基于不同DRL算法的DHO训练性能与收敛情况。