Multi-access Edge Computing(MEC)is one of the key technologies of the future 5G network.By deploying edge computing centers at the edge of wireless access network,the computation tasks can be offloaded to edge servers...Multi-access Edge Computing(MEC)is one of the key technologies of the future 5G network.By deploying edge computing centers at the edge of wireless access network,the computation tasks can be offloaded to edge servers rather than the remote cloud server to meet the requirements of 5G low-latency and high-reliability application scenarios.Meanwhile,with the development of IOV(Internet of Vehicles)technology,various delay-sensitive and compute-intensive in-vehicle applications continue to appear.Compared with traditional Internet business,these computation tasks have higher processing priority and lower delay requirements.In this paper,we design a 5G-based vehicle-aware Multi-access Edge Computing network(VAMECN)and propose a joint optimization problem of minimizing total system cost.In view of the problem,a deep reinforcement learningbased joint computation offloading and task migration optimization(JCOTM)algorithm is proposed,considering the influences of multiple factors such as concurrent multiple computation tasks,system computing resources distribution,and network communication bandwidth.And,the mixed integer nonlinear programming problem is described as a Markov Decision Process.Experiments show that our proposed algorithm can effectively reduce task processing delay and equipment energy consumption,optimize computing offloading and resource allocation schemes,and improve system resource utilization,compared with other computing offloading policies.展开更多
We investigate the electron retroreflection and the Klein tunneling across a graphene-based n-p-n junction irradiated by linearly polarized off-resonant light with the polarization along the x direction.The linearly p...We investigate the electron retroreflection and the Klein tunneling across a graphene-based n-p-n junction irradiated by linearly polarized off-resonant light with the polarization along the x direction.The linearly polarized off-resonant light modifies the band structure of graphene,which leads to the anisotropy of band structure.By adjusting the linearly polarized light and the direction of n-p-n junction simultaneously,the electron retroreflection appears and the anomalous Klein tunneling,the perfect transmission at a nonzero incident angle regardless of the width and height of potential barrier,happens,which arises from the fact that the light-induced anisotropic band structure changes the relation of wavevector and velocity of electron.Our finding provides an alternative and flexible method to modulate electron retroreflection and Klein tunneling.展开更多
基金supported in part by the National Key R&D Program of China under Grant 2018YFC0831502.
文摘Multi-access Edge Computing(MEC)is one of the key technologies of the future 5G network.By deploying edge computing centers at the edge of wireless access network,the computation tasks can be offloaded to edge servers rather than the remote cloud server to meet the requirements of 5G low-latency and high-reliability application scenarios.Meanwhile,with the development of IOV(Internet of Vehicles)technology,various delay-sensitive and compute-intensive in-vehicle applications continue to appear.Compared with traditional Internet business,these computation tasks have higher processing priority and lower delay requirements.In this paper,we design a 5G-based vehicle-aware Multi-access Edge Computing network(VAMECN)and propose a joint optimization problem of minimizing total system cost.In view of the problem,a deep reinforcement learningbased joint computation offloading and task migration optimization(JCOTM)algorithm is proposed,considering the influences of multiple factors such as concurrent multiple computation tasks,system computing resources distribution,and network communication bandwidth.And,the mixed integer nonlinear programming problem is described as a Markov Decision Process.Experiments show that our proposed algorithm can effectively reduce task processing delay and equipment energy consumption,optimize computing offloading and resource allocation schemes,and improve system resource utilization,compared with other computing offloading policies.
基金supported by the National Natural Science Foundation of China(Grant Nos.11804167,11804291,and 11904175)the Natural Science Foundation of Jiangsu Province,China(Grant Nos.BK20180739,BK20180740,and BK20180890)+2 种基金the Innovation Research Project of Jiangsu Province,China(Grant No.CZ0070619002)NJUPT-SF(Grant Nos.NY218128 and NY218135)NJUPT-STITP(Grant No.XYB2020301)。
文摘We investigate the electron retroreflection and the Klein tunneling across a graphene-based n-p-n junction irradiated by linearly polarized off-resonant light with the polarization along the x direction.The linearly polarized off-resonant light modifies the band structure of graphene,which leads to the anisotropy of band structure.By adjusting the linearly polarized light and the direction of n-p-n junction simultaneously,the electron retroreflection appears and the anomalous Klein tunneling,the perfect transmission at a nonzero incident angle regardless of the width and height of potential barrier,happens,which arises from the fact that the light-induced anisotropic band structure changes the relation of wavevector and velocity of electron.Our finding provides an alternative and flexible method to modulate electron retroreflection and Klein tunneling.
