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针对移动边缘计算(Mobile Edge Computing, MEC)网络中由于用户设备电池容量和任务处理能力有限导致的服务性能受限的问题，提出一种基于反向散射(Backscatter Communication, BackCom)无人机(Unmanned Aerial Vehicle, UAV)辅助MEC网络能耗最小化方案。该方案采用BackCom和主动传输混合卸载技术，建立一个包括计算资源调度和UAV轨迹的非凸优化问题，结合块坐标下降法和连续凸近似算法获取原问题的最优解。仿真结果表明，所提方案在最大限度降低能耗的同时，有效地降低了数据处理延迟，且在不同参数下均有效。
Abstract：For the issue of limited service performance in mobile edge computing(MEC) networks caused by the constraints of user device battery capacity and task processing capability, a solution for energy minimization in MEC networks is proposed, which adopts unmanned aerial vehicles(UAVs) based on backscatter communication(BackCom). A non-convex optimization problem including computational resource scheduling and UAV trajectory is established by using a hybrid offload technique of BackCom and active transport. The optimal solution of the original problem is obtained by combining block coordinate descent method and continuous convex approximation technique. Simulation results show that the proposed scheme can effectively reduce the data processing delay while minimizing the energy consumption, and it is effective under different parameters.
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基本信息:
DOI：10.13682/j.issn.2095-6533.2024.06.001
中图分类号:V279;TN929.5
引用信息:
[1]刘伯阳,党儒鸽,王丽平等.基于反向散射无人机辅助MEC网络能耗最小化方案[J].西安邮电大学学报,2024,29(06):1-10.DOI:10.13682/j.issn.2095-6533.2024.06.001.
基金信息:
国家自然科学基金项目(61901366); 陕西省自然科学基础研究计划项目(2020JQ-851); 陕西省普通高校青年杰出人才支持计划项目