基于MAB的组合联合通道和扩散因子选择,用于LoRa设备
Ikumi Urabe1, Aohan Li1,2, Minoru Fujisawa1
1Department of Electrical Engineering, Tokyo University of Science, Tokyo 125-8585, Japan.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
优化远程 (LoRa) 设备通信包括选择最佳的扩散因子 (SF) 和通道. 这项研究表明,结合SF和通道选择,考虑到设备位置,显著提高了LoRa系统的性能和可靠性.
科学领域:
- 无线通信无线通信
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
- 机器学习 机器学习
背景情况:
- 远程 (LoRa) 设备对于低功耗,远程物联网通信至关重要.
- 洛拉系统的可扩展性和性能在很大程度上取决于扩散因子 (SF) 和通道分配.
- 现有的方法经常独立地对待SF和频道选择,忽视它们的相互依赖性和位置影响.
研究的目的:
- 评估LoRa设备位置对实用,大规模系统中通信性能的影响.
- 实施和评估基于学习的联合道和SF选择方法.
- 提出和比较一种新的组合式多武器盗方法,用于联合SF和频道选择.
主要方法:
- 实施并评估了基于学习的去中心化联合道和SF选择算法 (Tug of War,UCB1, ε-greedy).
- 开发了一种组合式多臂强盗方法,其中SF和通道组合形成了"手臂".
- 基于ACKnowledge (ACK) 信息的评估方法,考虑设备位置和SF/通道相互依赖.
主要成果:
- 组合方法在框架成功率 (FSR) 和公平性方面明显优于独立的SF/通道选择方法.
- 与单独选择SF相比,联合通道和SF选择可以增强FSR.
- 最佳的通道和SF选择高度依赖于LoRa设备的空间分布 (位置情况).
结论:
- 位置意识,联合通道和SF选择对于优化LoRa系统性能至关重要.
- 拟议的组合多武器盗方法为密集的LoRa网络提供了卓越的可扩展性和可靠性.
- 未来的研究应该进一步探索适应性策略,根据实时网络条件和设备位置动态调整SF和通道.
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