As the 4th industrial revolution using information becomes an issue, wireless communication technologies such as the Internet of Things have been spotlighted. Therefore, much research is needed to satisfy the technological demands for the future society. A LPWA (low power wide area) in the wireless communication environment enables low-power, long-distance communication to meet various application requirements that conventional wireless communications have been difficult to meet. We propose a method to consume the minimum transmission power relative to the maximum data rate with the target of LoRaWAN among LPWA networks. Reinforcement learning is adopted to find the appropriate parameter values for the minimum transmission power. With deep reinforcement learning, we address the LoRaWAN problem with the goal of optimizing the distribution of network resources such as spreading factor, transmission power, and channel. By creating a number of deep reinforcement learning agents that match the terminal nodes in the network server, the optimal transmission parameters are provided to the terminal nodes. The simulation results show that the proposed method is about 15% better than the existing ADR (adaptive data rate) MAX of LoRaWAN in terms of throughput relative to energy transmission.

随着第四次使用信息的工业革命成为一个问题，诸如物联网之类的无线通信技术受到关注。因此，需要大量的研究来满足对未来社会的技术要求。无线通信环境中的LPWA（低功率广域网）使低功率，长距离通信能够满足传统无线通信难以满足的各种应用需求。我们提出了一种在LPWA网络中以LoRaWAN为目标，消耗相对于最大数据速率的最小传输功率的方法。采用强化学习来找到最小传输功率的合适参数值。通过深度强化学习，我们以优化网络资源（例如扩频因子，传输功率和信道）的分配为目标解决LoRaWAN问题。通过创建许多与网络服务器中的终端节点匹配的深度强化学习代理，可以将最佳传输参数提供给终端节点。仿真结果表明，相对于能量传输，所提方法比LoRaWAN的现有ADR（自适应数据速率）MAX高出约15％。