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Dynamic computation offloading in time-varying environment for ultra-dense networks:a stochastic game approach 认领 引用
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作者 Xie Renchao Liu Xu +3 位作者 Duan Xuefei Tang Qinqin Yu Fei Richard Huang Tao 《The Journal of China Universities of Posts and Telecommunications》 EI CSCD 2021年第2期24-37,共14页
To meet the demands of large-scale user access with computation-intensive and delay-sensitive applications,combining ultra-dense networks(UDNs)and mobile edge computing(MEC)are considered as important solutions.In the... To meet the demands of large-scale user access with computation-intensive and delay-sensitive applications,combining ultra-dense networks(UDNs)and mobile edge computing(MEC)are considered as important solutions.In the MEC enabled UDNs,one of the most important issues is computation offloading.Although a number of work have been done toward this issue,the problem of dynamic computation offloading in time-varying environment,especially the dynamic computation offloading problem for multi-user,has not been fully considered.Therefore,in order to fill this gap,the dynamic computation offloading problem in time-varying environment for multi-user is considered in this paper.By considering the dynamic changes of channel state and users’queue state,the dynamic computation offloading problem for multi-user is formulated as a stochastic game,which aims to optimize the delay and packet loss rate of users.To find the optimal solution of the formulated optimization problem,Nash Q-learning(NQLN)algorithm is proposed which can be quickly converged to a Nash equilibrium solution.Finally,extensive simulation results are presented to demonstrate the superiority of NQLN algorithm.It is shown that NQLN algorithm has better optimization performance than the benchmark schemes. 展开更多
关键词 dynamic computation offloading time-varying environment stochastic game ultra-dense networks(UDNs) mobile edge computing(MEC)
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