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将超密集网络(Ultra Dense Network, UDN)技术应用于多接入边缘计算(Muti-Access Edge Computing, MEC),通过密集化部署小基站与边缘服务器，提升了系统容量。大量用户的接入带来的计算与通信资源不足、用户之间产生严重干扰等问题，使得任务卸载过程面临巨大挑战，制定合理的卸载方案显得尤为重要。针对上述问题，联合考虑了计算与通信资源分配、功率控制，提出了基于混合遗传模拟退火算法(Hybrid Genetic Simulated Annealing Algorithm, HGSAA)的任务卸载方法。通过实验仿真表明，所提方法与其他传统方法相比，提高了用户的卸载效益。
Abstract：Ultra Dense Network(UDN) is applied to Muti-Access Edge Computing(MEC), and the system capacity is improved by the dense deployment of small base stations and edge servers. However, due to the lack of computing and communication resources and the serious interference between users brought by the access of a large number of users, the task uninstallation process faces great challenges. Therefore, it is particularly important to formulate a reasonable uninstallation scheme. To solve these problems, a task offloading method based on Hybrid Genetic Simulated Annealing Algorithm(HGSAA) is proposed by considering the allocation of computing and communication resources and power control. The experimental simulation shows that the proposed method improves the unloading efficiency of users compared with other traditional methods.
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基本信息:
DOI：
中图分类号:TN929.5
引用信息:
[1]唐煜星,王素红,韦睿等.超密集边缘计算网络下的任务卸载与资源分配[J].无线电工程,2023,53(09):2036-2045.
基金信息:
国家自然科学基金(61761007)~~