Obstacle-Aware UAV Swarm Deployment for User Coverage Using Deep Reinforcement Learning

During military operations, Unmanned Aerial Vehicles (UAVs) can act as aerial base stations to provide temporary communication services for ground users. Modern warfare often takes place in urban environments, where obstacles like trees and buildings can impede signal propagation, leading to reduced communication quality. This paper introduces an innovative approach using UAVs' observations of surrounding obstacles to make informed decisions about their movements for improved user coverage. We use Deep Reinforcement Learning (DRL) in a multi-agent setting to integrate real-time obstacle observations and cooperative strategies among UAVs for enhanced Line-of-Sight (LoS) connections essential for effective communication, establishing reliable communication networks in urban environments. The DRL-based decision-making framework enables UAVs to dynamically adjust their positions to achieve near-optimal user coverage, representing an advancement in urban warfare communication technology. Simulation results show that UAVs with the DRL-based decision-making framework achieve significantly improved coverage and shorter travel distances for enhanced operational efficiency in dense urban scenarios compared with baselines without considering obstacle observations.