多次用户认知边缘计算网络资源分配方案

Resource allocation scheme for multip-secondary user cognitive mobile edge computing networks

为了缓解当前边缘计算网络中资源优化方案长期收益有限与频谱效率低的问题,基于强化学习,提出一种多次用户认知边缘计算网络资源分配方案。建立了一个认知边缘计算网络资源分配优化设计模型,该模型由多个次用户、一个主用户以及一个小基站构成。次用户通过小基站对主用户状态进行频谱感知,采用时分多址技术接入主用户频谱进行任务卸载。利用部分可观测马尔科夫决策过程对认知边缘计算网络中的次用户信道接入时间比例、边缘计算能耗、CPU计算频率与任务卸载功率进行联合优化设计,最大化次用户能获得的加权期望计算比特数之和。仿真结果表明,相比只考虑单个时隙性能最优的传统算法,所提方案显著提升了网络中次级用户长期期望计算比特数。

In order to alleviate the limited long-term benefits of current resource optimization schemes and the low spectral efficiency of edge computing networks,a resource allocation scheme for multiple secondary user cognitive mobile edge computing network is proposed based on the reinforcement learning.A cognitive based mobile edge computing resource allocation scheme optimization model is studied,which is composed of multiple secondary users,one primary user and one small base station.The secondary users sense the status of the primary user through the small base station by spectrum sensing,and adopt time division multiple address technology to access the spectrum of the primary user for tasks offloading.The partial observable Markov decision process is adopted to jointly optimize the ratio of channel access time,computing energy consumption,CPU computing frequency and task offloading power of the secondary users.The goal is to maximize the long term expected weighted sum of calculated bits number of secondary users.Simulation results show that the proposed scheme significantly improves overall long-term expected calculated number of bits of secondary users than the traditional algorithm,which only considers the optimal performance during a single time slot.