Optimal Transmission Control and Learning-Based Trajectory Design for UAV-Assisted Detection and Communication

Due to their high mobility, flexible deployment and stable maneuverability, unmanned aerial vehicles (UAVs) have been deemed as a promising and indispensable role for various emerging applications (e.g., dangerous area detection, dynamic target tracking, and map remote sensing). Compared to the static monitoring equipments, UAV-mounted high-definition camera and signal transceiver can be used cost-effectively as an on-demand aerial platform to detect the unknown region and send the real-time data back at the same time. However, these highlighted limitations of battery capacity and communication resource extremely affect the UAV's performance such as flight endurance and data transmission. Motivated by the above conflicts, this paper aims to minimize the total energy consumed by the UAV during the region detection mission through jointly optimizing the collected data size, transmission time, and flying trajectory. Toward this end, we derive the optimal data collection and transmission time in closed forms via convex optimization, and propose a model-free reinforcement learning-based algorithm for training the UAV to plan its trajectory without knowing the environment information in advance. Simulation results validate the performance of our designs in terms of convergence, energy consumption, and energy efficiency.