Long Range (LoRa) wireless technology, characterized by low power consumption and a long communication range, is regarded as one of the enabling technologies for the Industrial Internet of Things (IIoT). However, as the network scale increases, the energy efficiency (EE) of LoRa networks decreases sharply due to severe packet collisions. To address this issue, it is essential to appropriately assign transmission parameters such as the spreading factor and transmission power for each end device (ED). However, due to the sporadic traffic and low duty cycle of LoRa networks, evaluating the system EE performance under different parameter settings is time-consuming. Therefore, we first formulate an analytical model to calculate the system EE. On this basis, we propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa) with the aim of maximizing the system EE of LoRa networks. Notably, MALoRa employs an attention mechanism to guide each ED to better learn how much ''attention'' should be given to the parameter assignments for relevant EDs when seeking to improve the system EE. Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms with an acceptable degradation in packet delivery rate (PDR).

翻译：长距离无线技术LoRa（Long Range）以其低功耗和长通信距离的特性，被视为工业物联网（IIoT）的关键支撑技术之一。然而，随着网络规模扩大，数据包碰撞问题加剧导致LoRa网络能效（EE）急剧下降。解决该问题的关键在于为每个终端设备（ED）合理分配扩频因子和发射功率等传输参数。但由于LoRa网络业务具有突发性和低占空比特性，评估不同参数配置下的系统能效非常耗时。为此，我们首先构建用于计算系统能效的解析模型，进而提出基于多智能体强化学习（MALoRa）的传输参数分配算法，以最大化LoRa网络系统能效。值得注意的是，MALoRa采用注意力机制引导每个终端设备在优化系统能效时，更好地学习应给予相关ED参数分配何种程度的"关注"。仿真结果表明，与基准算法相比，MALoRa在可接受的数据包送达率（PDR）降低范围内显著提升了系统能效。