计算机工程2025，Vol.51Issue(5)：1-8,8.DOI:10.19678/j.issn.1000-3428.0069423
空地算力网络中的异构资源协同优化
Collaborative Optimization of Heterogeneous Resources in Air-Ground Computing Power Networks
摘要
Abstract
To address the challenges of insufficient computing capacity of end users and the unbalanced distribution of computing power among edge nodes in computing power networks,this study proposes an Unmanned Aerial Vehicle(UAV)-assisted Device-to-Device(D2D)edge computing solution based on incentive mechanisms.First,under constraints involving computing resources,transmission power,and the unit pricing of computing resources,a unified optimization problem is formulated to maximize system revenue.This problem aims to optimize the task offloading ratio,computing resource constraints,UAV trajectory,as well as the transmission power and unit pricing of computing resources for both UAVs and users.The Proximal Policy Optimization(PPO)algorithm is employed to establish user offloading and purchasing strategies.In addition,an iterative strategy is implemented at each time step to solve the optimization problem and obtain the optimal solution.The simulation results demonstrate that the PPO-based system revenue maximization algorithm exhibits superior convergence and improves overall system revenue compared to the baseline algorithm.关键词
空地算力网络/激励机制/终端直连通信/计算卸载/近端策略优化Key words
air-ground computing power network/incentive mechanism/Device-to-Device(D2D)communication/computation offloading/Proximal Policy Optimization(PPO)分类
信息技术与安全科学引用本文复制引用
李斌,山慧敏..空地算力网络中的异构资源协同优化[J].计算机工程,2025,51(5):1-8,8.基金项目
国家自然科学基金(62101277). （62101277）
