Deep Neural Networks-based Intelligent UAV Trajectory Planning in 5G and Beyond Non-Terrestrial Communication Networks

In recent times, unmanned aerial vehicles (UAVs) have been used in many real-world applications such as trans-portations and delivery, agriculture and forestry, infrastructure inspection and surveying, surveillance, and counter-terrorism, wireless communications, and emergency and disaster management. For their role in disaster prevention and control, UAV operations as flying base stations (FBSs) have become pivotal in enhancing network coverage and quality of service. Therefore, autonomous FBS deployment has gained significant research interest due to UAV control and manoeuvring in dynamic and challenging environments. Further, these deployments hinge on efficient trajectory planning along with obstacle avoidance to reach their destination without depleting limited battery resources. To tackle this challenge, wireless channel characteristics such as channel state information (CSI) can help UAVs avoid obstacles and reach their destination point. In this work, we propose a novel deep learning-based FBS trajectory prediction framework that uses CSI information to dynamically learn environmental characteristics while avoiding obstacles and minimising flight time. By integrating a recurrent convolutional neural network (RCNN) architecture, our framework achieves a remarkable 98.34% prediction accuracy in identifying optimal trajectories, significantly outperforming deep reinforcement learning (DRL)-based 85.42% and traditional machine learning (ML)-based schemes 93.25%. Extensive simulations demonstrate that our approach reduces the flight time of the FBS by nearly 44 seconds (47.4%) compared to existing methods under obstacle-based conditions. Our approach demonstrates energy-efficient FBS trajectory planning and underscores its significance for applications in wireless communications, emergency response, and disaster management.