Channel-Occupation-Aware Resource Allocation in LoRa Networks: a DQN-and-Optimization-Aided Approach

Long Range (LoRa) technology which provides low power consumption and long transmission range is regarded as one of the key technologies for industrial Internet of Things (IIoTs). In this article, we investigate an energy efficiency (EE) maximization problem through the joint allocation of spreading factors (SFs), channels (CHs) and transmit power. Then, the optimal solution is derived by decomposing the problem into two stages: (i) a deep Q-Network (DQN) is trained to generate the allocation of CH/SF based on the channel state information (CSI) collected from end-devices (EDs); (ii) given the CH/SF allocation, the optimal transmission power is then determined by solving a convex optimization problem. Simulation results demonstrate that our algorithm is superior to the random CH/SF and power selecting method, and achieves a near-optimal performance.