Integrating UAVs and D2D Communication for MEC Network: A Collaborative Approach to Caching and Computation

Abstract

Mobile edge computing (MEC) with unmanned aerial vehicles (UAVs) extends the range of computational services. However, operating UAVs independently can limit their effectiveness. Efficient edge computing requires storing additional data or programs on edge servers, making collaborative computing and caching essential. To address these challenges, we propose a collaborative framework that leverages device-to-device (D2D) communication and multiple UAVs to enhance MEC performance. We aim to minimize the system average latency by jointly optimizing content caching, task offloading, UAV trajectory planning, and computational resource allocation. The problem is formulated as a mixed-integer non-convex optimization problem and decomposed into three subproblems. An iterative algorithm based on penalty successive convex approximation (P-SCA) and Lagrangian duality is developed to alternately optimize these subproblems until convergence. Extensive simulations demonstrate that our algorithm significantly reduces system average latency and enhances network performance across various scenarios.