用强化学习优化低轨卫星切换，兼顾速度、成功率和成本。
Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks
- 基于双网络强化学习动态权衡切换指标
- 吞吐量提升最高达10.3%，阻塞率接近零
- 适合需要稳定连接的实时通信场景
本文提出一种基于双网络深度强化学习（Dueling DDQN）的自适应多目标切换框架，用于低地球轨道（LEO）卫星网络。该方法可在时变网络条件下，实现吞吐量、阻塞概率与切换开销之间的动态权衡。仿真结果表明，所提方法在典型运行条件下持续优于传统基线方案，吞吐量最高提升10.3%，阻塞概率趋近于零。
原文摘要 · Abstract (English)
In this paper, we propose a dueling double deep Q-network (DDQN)-based adaptive multi-objective handover framework for low Earth orbit (LEO) satellite networks. The proposed method enables dynamic trade-off learning among throughput, blocking probability, and switching cost under time-varying network conditions. Simulation results demonstrate that the proposed approach consistently outperforms conventional baselines, achieving up to 10.3% throughput improvement and near-zero blocking under typical operating conditions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。