面向6G MEC多业务通信与计算资源联合优化策略  

Joint optimization strategy for 6G MEC multi-service communication and computing resources

作　　者：王德胜 邓珂 黄治华[3] 张浩 林浩瀚 WANG Desheng;DENG Ke;HUANG Zhihua;ZHANG Hao;LIN Haohan(School of Electronic Information and Communications,Huazhong University of Science and Technology,Wuhan 430074,China;Department of Broadband Communication,Pengcheng Laboratory,Shenzhen 518055,Guangdong China;Wuhan Maritime Communication Research Institute,Wuhan 430072,China)

机构地区：[1]华中科技大学电子信息与通信学院,湖北武汉430074 [2]鹏城实验室宽带通信研究部,广东深圳518055 [3]武汉船舶通信研究所,湖北武汉430072

出　　处：《华中科技大学学报（自然科学版）》2023年第3期1-6,16,共7页Journal of Huazhong University of Science and Technology(Natural Science Edition)

基　　金：国家重点研发计划资助项目(2020YFB1806904);国家自然科学基金资助项目(62071192);湖北省重点研发计划资助项目(2022BAA006)。

摘　　要：以边缘计算技术(MEC)的系统时延和能耗作为联合优化的核心指标,对多小区-多终端场景进行系统性混合整数非线性规划(MINLP)的数学建模,利用遗传算法中嵌套麻雀觅食算法的思路,建立从整体到局部的联合优化方法;通过分析通信资源与计算资源二者之间的置换机理,给出了高效的终端接入选择、任务卸载策略,以及发射功率、计算频率等优化方法.为未来新型业务在时效与能耗等优化方面起到积极的理论指导作用,也为未来6G通信、计算、存储、控制等4C一体化设计提供有益探索.Aim to achieve the core indexes of mobile edge computing(MEC) time delay and energy consumption joint optimization,a mixed-integer nonlinear programming(MINLP) model was provided under the multi-cell-multi-terminal scenarios.By using of embedding sparrow foraging algorithm into genetic algorithm,an optimization method from the whole to the local was established. And then, a replacement mechanism between communication resources and computing resources was analyzed.Moreover, a serial of scheduling method, including efficient terminal access selection,task unloading strategy, transmitting power, computing frequency and so on, were deduced. This work, as a significant exploration for the integration design, will benefit the optimization of efficiency and energy consumption for the future 6G service.

关 键 词：6G 移动边缘计算 通信计算一体化 资源分配 遗传算法 麻雀算法 

分 类 号：TN929.531[电子电信—通信与信息系统]