Resource Allocation in UAV Edge Computing Communication System

Unmanned Aerial Vehicle (UAV) communication holds paramount importance in modern communication systems, especially in scenarios like real-time reconnaissance during natural disasters. However, the resource limitations of UAVs often hamper their ability to efficiently handle time-sensitive tasks. To augment the computational prowess of these aerial vehicles, we propose a novel approach involving a network of multiple UAVs deployed with Mobile Edge Computing (MEC) devices. This study delves into the deployment of MEC devices on UAV platforms, enabling task offloading for the swift processing of time-sensitive tasks across the entire UAV network. We formulate a problem focused on minimizing delays in UAV edge computing systems while adhering strictly to power constraints. However, the highly non-convex nature of this problem poses significant challenges in finding effective solutions. To overcome these challenges, we introduce a Penalty Dual Decomposition (PDD) method that iteratively tackles the problem, thereby deriving the optimal task offloading and transmission power allocation. Simulation results reveal that our proposed task offloading and transmission power optimization algorithm outperforms traditional methods, achieving a commendable 23.7% reduction in delay.