DDPG_CAD: Intelligent Channel Activity Detection Scheduling in Massive IoT LoRaWAN

Beyond 5G and future 6G aim to address rising energy demands as the world becomes more interconnected. LoRaWAN network is an energy-efficient IoT solution, but frequent retransmissions can quickly deplete sensor batteries. Efficient traffic management and collision avoidance are crucial. Initially, LoRaWAN used ALOHA for multi-access, causing increased network collisions. The recent innovation of Channel Activity Detection (CAD) has emerged to tackle these issues. CAD enhances multi-access by sensing channel activity before transmission. Although an improvement, CAD is not foolproof. Our paper introduces enhancements to CAD through the Deep Deterministic Policy Gradient-based Algorithm (DDPG_CAD). To assess CAD functionality, we develop LoRaCAD, a dedicated simulator. We also conduct a thorough comparative analysis of scheduling strategies, considering energy efficiency, latency, and packet delivery ratio.