Joint Optimization Of Task Offloading And Resource Allocation Strategies For Ultra-dense Networks In 5G

With the development of network technology and the popularization of communication equipment,a large number of Internet of things applications have emerged.However,the computing and caching resources of mobile devices are often limited by their own volume,and can not handle a large number of computing intensive and delay critical computing tasks,so cloud computing and even edge computing are proposed to deal with the above problems.Mobile devices can share computing resources by offloading some computing intensive and delay sensitive tasks to the cloud,which makes it possible to complete the arrived tasks under low latency.However,with the coming of 5g era,the Ultra-Dense Networks is gradually taking shape.Unlike the traditional cloud computing architecture,the Ultra-Dense Networks provides a large number of small base stations under the powerful Acer station,providing one-to-one or even many to one network services for mobile devices.The mobile devices can unload the computing tasks to the macro base stations and small base stations within the coverage at the same time.The Acer station has strong Large computing power but outstanding problem with cloud computing architecture: too far away from mobile devices,high transmission delay.The small base station has slightly less computing power than the Acer station,but it is close to the mobile device,the transmission delay and transmission energy consumption are greatly reduced,the network architecture has changed significantly,from one layer to multi-layer,and the calculation offload and resource allocation strategy under the super density network become one of the key problems of mobile edge computing.In recent years,with the increasing greenhouse effect,low-carbon environmental protection has become the theme of today's society,and the concept of green computing has also been put forward.In this paper,based on the principle of green computing,the energy consumption model of the system is established as a nonconvex problem with the purpose of minimizing the energy consumption.The problem is transformed into two subproblems,task unloading and computing communication resource allocation,which satisfy the convex optimization.The Lagrangian multiplier method is used to solve the problem alternately.The joint optimization model of task unloading and resource allocation strategy is proposed Type.And the validity of the algorithm is proved by experiment simulation.