北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2024, Vol. 47 ›› Issue (3): 96-102.

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空地协同隐蔽移动边缘计算系统的能耗优化算法

谢文婷,李安,杨鼎成,林庆庆   

  1. 南昌大学 信息工程学院
  • 收稿日期:2023-06-25 修回日期:2023-08-15 出版日期:2024-06-30 发布日期:2024-06-13
  • 通讯作者: 李安 E-mail:lian@ncu.edu.cn

Energy Consumption Optimization for Air-Ground Cooperation Based Covert Mobile Edge Computing System

  • Received:2023-06-25 Revised:2023-08-15 Online:2024-06-30 Published:2024-06-13

摘要: 针对实际应用中难以获取看守者Willie准确位置信息的问题,提出了鲁棒的空地协同隐蔽移动边缘计算系统的能耗优化算法。具体而言,在只知道看守者Willie估计位置信息条件下,为了隐藏无人机与地面基站之间的计算卸载行为,分别从看守者Willie和无人机的角度分析隐蔽性约束,然后联合优化计算任务分配因子、无人机-地面基站关联因子、无人机功率以及飞行轨迹以最小化无人机的加权总能耗。为了解决所提出的非凸优化问题,基于块坐标下降法、S-引理和连续凸近似法,提出了一种带参数的三阶段高效交替迭代优化算法。仿真结果表明,所提出算法能在节省能耗的同时增大给定隐蔽率约束下的卸载任务量,且具有良好的收敛性。

关键词: 隐蔽移动边缘计算, 空地协同, 无人机, 能耗优化, 交替迭代优化算法

Abstract: Aiming at the problem that it is difficult to obtain the accurate location information of the warden node Willie in practical applications, the robust energy consumption optimization algorithm for air-ground cooperation based covert mobile edge computing is presented. Specifically, under the condition that only the estimated location information of Willie is known, in order to robustly hide the computational offloading behavior between UAV and GBSs, the covertness constraints are analyzed from the perspective of the warden node Willie and the UAV, respectively. Then the weighted total energy consumption of the UAV is minimized by jointly optimizing the computation task allocation factor, the UAV-GBS association factor, the UAV power and the UAV trajectory. To tackle the formulated non-convex optimization problem, an efficient three-stage alternating iterative optimization algorithm with parameter based on block coordinate descent method, S-procedure and successive convex approximation method is proposed. Simulation results show that the proposed algorithm can save energy consumption and offload more computing tasks to the GBSs under given covert rate constraints, and has a desirable convergence.

Key words: covert mobile edge computing, air-ground cooperation, unmanned aerial vehicle, energy consumption optimization, iterative alternating optimization algorithm

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