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arXiv 2408.09109cs.NIcs.LG
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面向无人机辅助通信的基于改进Q学习的多跳路由

Improved Q-learning based Multi-hop Routing for UAV-Assisted Communication

International Institute of Information Technology Bangalore(班加罗尔国际信息技术学院)

N P Sharvari, Dibakar Das, Jyotsna Bapat, Debabrata Das

AI总结:

针对无人机辅助通信路由设计的挑战,提出基于改进Q学习的多跳路由算法IQMR,采用Q(λ)学习决策,无需预规划路径,在能效和吞吐量上较现有方法分别提升36.35%和32.05%。

摘要:

由于拓扑动态变化、电池容量有限以及通信环境的动态特性,设计有效的无人机(Unmanned Aerial Vehicle, UAV)辅助路由协议颇具挑战。现有协议优先优化单个网络参数,忽略了在连接间歇性中断、信号强度波动以及网络密度变化的场景中采用精细化方法的必要性,最终无法全面满足空中网络的需求。本文提出了一种新颖的、基于改进Q学习的多跳路由(Improved Q-learning-based Multi-hop Routing, IQMR)算法,用于实现最优的无人机辅助通信系统。IQMR采用Q(λ)学习进行路由决策,大幅提升了能量效率和网络数据吞吐量。在无需预定义无人机路径规划的前提下,IQMR通过优先保障可靠连接和无人机间避障,同时整合实时网络状态信息,提升了系统韧性,从而确保对不断变化的网络条件的动态适应性。结果验证了IQMR对变化的系统条件的适应性,以及相较于现有技术的优越性。IQMR在能量效率和数据吞吐量方面分别比现有方法提升了36.35%和32.05%。

英文摘要:

Designing effective Unmanned Aerial Vehicle(UAV)-assisted routing protocols is challenging due to changing topology, limited battery capacity, and the dynamic nature of communication environments. Current protocols prioritize optimizing individual network parameters, overlooking the necessity for a nuanced approach in scenarios with intermittent connectivity, fluctuating signal strength, and varying network densities, ultimately failing to address aerial network requirements comprehensively. This paper proposes a novel, Improved Q-learning-based Multi-hop Routing (IQMR) algorithm for optimal UAV-assisted communication systems. Using Q(λ) learning for routing decisions, IQMR substantially enhances energy efficiency and network data throughput. IQMR improves system resilience by prioritizing reliable connectivity and inter-UAV collision avoidance while integrating real-time network status information, all in the absence of predefined UAV path planning, thus ensuring dynamic adaptability to evolving network conditions. The results validate IQMR's adaptability to changing system conditions and superiority over the current techniques. IQMR showcases 36.35\% and 32.05\% improvements in energy efficiency and data throughput over the existing methods.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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