CORA: Channel Occupancy-Aware Resource Allocation in LoRa Wireless Networks

Long Range (LoRa) technology, characterized by extended transmission range and low power consumption, is considered a crucial technology for the Industrial Internet of Things (IIoT). However, with increasing network scale, existing LoRa networks have begun to exhibit low energy efficiency (EE) due to severe collisions. Existing methodologies predominantly assume perfect SF orthogonality, neglecting the detrimental effects of interference between signals using different spreading factors (SFs) in the same channel (CH). This study investigates a joint SF, CH, and transmit power (TP) allocation problem with the objective of optimizing the system's uplink EE while fully considering quasiorthogonal SFs, which is an NP-hard optimization problem. To enhance tractability, we decompose the original problem into two subproblems: CH/SF assignment and TP allocation. First, an attention-based deep neural network is designed to generate a near-optimal CH/SF assignment, leveraging both channel state information and channel activity detection-based historical channel occupancy information. Second, given the CH/SF assignment, the optimal TP allocation is determined through the resolution of a convex optimization problem. Experimental results on a real-world LoRa network demonstrate that our proposed method significantly improves the system's uplink EE.