MIX-MAB：基于强化学习的LoRaWAN资源分配算法
MIX-MAB: Reinforcement Learning-based Resource Allocation Algorithm for LoRaWAN
摘要：
本文主要从分组投递率（PDR）的角度对资源分配算法进行改进，远程广域网（LoRaWAN）中终端设备（ED）发送的成功接收数据包的数量。设置传输参数会显著影响PDR。采用强化学习（RL），我们提出了一个资源分配算法，使ED配置他们的传输参数以分布式的方式。我们的资源分配问题建模为多臂强盗（MAB），然后解决它提出了一个两阶段的算法名为混合MAB，它包括指数权重的勘探和开发（EXP 3）和连续消除（SE）算法。我们通过仿真结果评估了MIX-MAB的性能，并将其与其他现有方法进行了比较。数值结果表明，该方法在收敛时间和PDR方面都优于已有的方法。
英文摘要：
This paper focuses on improving the resource allocation algorithm in terms of packet delivery ratio (PDR), i.e., the number of successfully received packets sent by end devices (EDs) in a long-range wide-area network (LoRaWAN). Setting the transmission parameters significantly affects the PDR. Employing reinforcement learning (RL), we propose a resource allocation algorithm that enables the EDs to configure their transmission parameters in a distributed manner. We model the resource allocation problem as a multi-armed bandit (MAB) and then address it by proposing a two-phase algorithm named MIX-MAB, which consists of the exponential weights for exploration and exploitation (EXP3) and successive elimination (SE) algorithms. We evaluate the MIX-MAB performance through simulation results and compare it with other existing approaches. Numerical results show that the proposed solution performs better than the existing schemes in terms of convergence time and PDR.
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。
