Fast Learning for Optimization of Green Edge Collaborative UAV

Abstract

Unmanned aerial vehicles play an increasingly important role in the low-altitude domain by collecting and transmitting aerial images. However, the inter-dependency between UAV motion and communication strategies has been largely overlooked. To address this gap, we propose a UAV motion-aware image capturing and communication (MICC) system that dynamically optimizes data offloading by jointly considering scenario variability and communication resource allocation. Specifically, we formulate an MICC optimization problem to maximize transmission accuracy and efficiency by adaptively controlling down-sampling ratios, compression ratios, and transmit power. Considering its non-convex nature, we first develop a geometric programming based algorithm (GP-MICC) to obtain high-fidelity solutions. Recognizing its high computational cost, which hinders real-time deployment, we further propose a fast learning-based optimization algorithm (FLO-MICC). Extensive experiments demonstrate that GP-MICC achieves excellent transmission performance, while FLO-MICC reduces computational time by over 12x with minimal performance loss, making it suitable for dynamic UAV scenarios.