Matching-Driven Deep Reinforcement Learning for Improving Energy Efficiency in LoRa Networks

LoRa is considered one of the most promising low-power wide-area techniques. Given that end devices (EDs) are typically battery-powered, energy efficiency (EE) is a critical factor to consider. In this paper, we aim to improve the system EE of the LoRa network by jointly allocating transmission parameters such as the channel (CH), transmission power (TP) and spreading factor (SF) for each ED. Owing to the low duty cycle and sporadic traffic of LoRa networks, evaluating the system EE under various parameter settings proves to be time-consuming. Consequently, we propose an analytical model aimed at calculating the system EE while fully considering the impact of multiple gateways, quasi-orthogonal SFs and capture effects. On this basis, we investigate a joint CH, SF and TP allocation problem to optimize the system EE for uplink transmissions. Given the NP-hard complexity of the problem, the original problem is decomposed into two subproblems: CH assignment and SF/TP assignment. First, a matching-based algorithm is introduced to tackle the CH assignment subproblem. Then, an attention-based multiagent reinforcement learning technique is employed to address the SF/TP assignment subproblem for EDs allocated to the same CH. The simulation outcomes indicate that the proposed approach converges quickly and obtains significantly better system EE than baseline algorithms.