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首页> 外文会议>International conference on human centered computing >Reinforcement Learning Based Signal Quality Aware Handover Scheme for LEO Satellite Communication Networks
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Reinforcement Learning Based Signal Quality Aware Handover Scheme for LEO Satellite Communication Networks

机译：基于LEO卫星通信网络的强化基于信号质量意识的切换方案

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摘要

With the increasing of Low Earth Orbit (LEO) satellites emission, utilizing existing LEO satellite network systems have lower CAPEX/OPEX than deploying fixed terrestrial network systems in the remote area. Due to the high mobility of LEO satellites, mobility management mechanisms such as handover schemes are the key issue should be settled on leveraging the merits of LEO satellites telecommunications (e.g. lower propagation delay than GEO satellites). In traditional handover schemes, choosing which one is the next-hop satellite for one user is only determined by evaluating some specific criteria in the current state, not guaranteeing long-term and global optimization. To solve this problem, we use the cumulative signal quality that involves the remaining service time and signal quality and propose a Q-Learning based handover scheme. The simulation results show that the proposed scheme improves the overall signal quality and reduce the average handover number of users compared with other handover schemes.
机译：随着低地球轨道（LEO）卫星发射的增加，利用现有的LEO卫星网络系统具有较低的CAPEX / OPEX，而不是部署偏远地区的固定地面网络系统。由于Leo卫星的高流动性，移动计划等移动性管理机制是关键问题，应解决利用Leo卫星电信的优点（例如，比Geo卫星低的传播延迟）。在传统的切换方案中，选择哪一个是一个用户的下一跳卫星仅通过评估当前状态的一些特定标准来确定，而不是保证长期和全局优化。为了解决这个问题，我们使用累积信号质量，涉及剩余的服务时间和信号质量，并提出基于Q学习的切换方案。仿真结果表明，与其他切换方案相比，该方案提高了整体信号质量，降低了用户的平均切换次数。

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