UAV Data Collection With Deep Reinforcement Learning for Grant-Free IoT

The utilization of unmanned aerial vehicles (UAVs) for efficient data collection has gained considerable attention. In this paper, we examine a scenario involving grant-free access from Internet of Things (IoT) devices, where the random access may cause packet collision, stemming from multiple devices concurrently transmitting data. To address this issue, we propose a deep reinforcement learning-based collision avoidance (DRL-CA) approach for UAV data collection, which optimizes the UAV trajectory. The approach assists UAVs in identifying and maximizing the acquisition of device packet in an environment characterized by probabilistic packet transmission and potential collisions among device packets while ensuring a timely arrival at the destination. Through simulations, our proposed method effectively mitigates unnecessary conflicts among device packets while achieving the optimization objective.