A Reinforcement Learning Based Medium Access Control Method for LoRa Networks

Abstract

LoRa is a low-power long-range network technology, which is used widely in power sensitive and maintenance free Internet of Things applications. LoRa only defines the physical layer protocol, while LoRaWAN is a medium access control (MAC) layer protocol above it. However, simply using ALOHA in LoRaWAN makes a high package collision rate when the number of the end-devices in the network is large, since many end-devices will send the packages to gateway at the same time. To solve this, we present a reinforcement learning (RL) based multi access method for LoRaWAN, which allows end-devices decide when to transmit data based on the environment and reduce the package collision rate. A comparation between the RL method and ALOHA is also included in the paper, which shows that the RL method has a lower package collision rate.