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.

本文提出了一种分布式资源分配算法，利用强化学习的思想，将资源分配问题建模成多臂老虎机问题，并通过 MIX-MAB 算法解决，仿真结果表明该算法在收敛时间和数据包传输成功率等方面表现优异。