Fully Autonomous Distributed Transmission Parameter Selection Method for Mobile IoT Applications Using Deep Reinforcement Learning

With the rapid increase in Internet of Things (IoT) devices, packet collision has become a serious problem in Long Range (LoRa) communication. Furthermore, the rising demand for mobile IoT applications necessitates parameter allocation methods that take into account the mobility of End Devices (EDs). However, the existing transmission parameter selection methods considering mobility are almost centralized designs, which may increase the probability of interference and system latency. The decentralized design is few and requires prior information on ED, which may cause scalability issues, e.g., adding ED to an existing network. To solve the problems above, a method through a fully autonomous distributed design supposing the mobility of ED using Double Deep Q Network (DDQN) is proposed in this paper. In the proposed method, ED trains the model based on ACKnowledge (ACK) packets and its location information without any prior information. Performance evaluation results show that the proposed method can achieve a higher packet delivery rate than other autonomous distributed methods in mobile IoT scenarios.