PAMLR: A Passive-Active Multi-Arm Bandit-Based Solution for LoRa Channel Allocation
PAMLR: A Passive-Active Multi-Arm Bandit-Based Solution for LoRa Channel Allocation
复制标题
DOI：
10.1145/3600100.3623725
复制
发表时间：
2023-11
期刊：
影响因子：
--
通讯作者：
Jihoon Yun;Chengzhang Li;Anish Arora
中科院分区：
文献类型：
--
作者：
Jihoon Yun;Chengzhang Li;Anish Arora
在城市环境中的低功率无线网络中实现低占空比操作由于外部干扰和衰落的复杂和可变动态而变得复杂。我们探索使用强化学习来实现低功耗的任务，最佳选择通道。该学习依赖于用于处理外部干扰的被动信道采样和用于处理衰落的主动信道采样的混合。我们的解决方案，LoRa的被动-主动多臂强盗（PAMLR，发音为“Pamela”），平衡了两种类型的样本，以实现节能的信道选择：主动信道测量被调谐到适当的低水平，以更新噪声阈值，并补偿被动信道测量被调谐到适当的高水平，以选择使用噪声阈值从信道探索的最顶端的信道。两种类型的样本的速率响应于信道动态而被适配。基于在不同城市的多个环境中进行的广泛测试，我们验证了PAMLR可以保持优异的通信质量，如与最优信道分配策略相比，低SNR遗憾所示，同时大大降低了与信道测量相关的能量成本。
Achieving low duty cycle operation in low-power wireless networks in urban environments is complicated by the complex and variable dynamics of external interference and fading. We explore the use of reinforcement learning for achieving low power consumption for the task of optimal selection of channels. The learning relies on a hybrid of passive channel sampling for dealing with external interference and active channel sampling for dealing with fading. Our solution, Passive-Active Multi-armed bandit for LoRa (PAMLR, pronounced “Pamela”), balances the two types of samples to achieve energy-efficient channel selection: active channel measurements are tuned to an appropriately low level to update noise thresholds, and to compensate passive channel measurements are tuned to an appropriately high level for selecting the top-most channels from channel exploration using the noise thresholds. The rates of both types of samples are adapted in response to channel dynamics. Based on extensive testing in multiple environments in different cities, we validate that PAMLR can maintain excellent communication quality, as demonstrated by a low SNR regret compared to the optimal channel allocation policy, while substantially minimizing the energy cost associated with channel measurements.