Research On Energy Efficiency Optimization Algorithm In Ultra Dense Heterogeneous Networks With Backhaul-Constrained

The explosive growth of network traffic in the future has caused widespread concern in the development of communication network technology.And ultra-dense heterogeneous cellular networks will be a promising technology in the future by deploying dense small base stations and smaller distances between terminals and nodes.Therefore,it can achieve higher spectral efficiency,greater coverage,and expand system capacity.However,the problem of backhaul-canstrained in ultra-dense heterogeneous networks restricts the transmission of data,which is a major bottleneck for improving system throughput growth.In addition,due to the increasingly dense deployment of small base stations and the improvement of mobile device's intelligence,the problem of increasing energy consumption and insufficient power supply has become increasingly prominent.These two major problems are bottlenecks that hinder the development of ultra-dense heterogeneous networks.Therefore,how to improve the energy efficiency under limited backhaul capacity is a hotspot of ultra-dense heterogeneous networks.First of all,the energy efficiency of small base stations is studied in this thesis for the problem of backhaul-constrained bottlenecks and the lack of energy of small base stations.In order to solve the problem that energy efficient of the small base station is not high due to the constrained backhaul,an energy efficiency optimization algorithm based on the factor for the portion of time division(FPTD)in backhaul-constrained ultra dense HetNets is proposed in this thesis.The optimization problem considers a joint design of downlink precoding,power allocation and the FPTD to establish energy efficiency optimization mathematical model.Meanwhile,we utilize first order Taylor convex approximation(FOTCA),convex slack variable and one-dimensional search method(ODS)to solve and improve the energy efficiency.Simulation results show that the proposed FPTD-based energy efficiency optimization algorithm(FEEOA)is improved with better energy efficient compared with the primitive scheme without FPTD,and more throughputs can be transmitted at the same energy consumption.MEC(mobile edge computing)technology can break the bottleneck of the backhaul-constrained by arranging servers at the edge of the cell,so it can provide services to users without a large number of backhaul.Therefore,ultra-dense HetNets with MEC play an important role in breaking bottleneck of the backhaul-constrained.However,due to the deepening of the mobile device's intelligence and the completion of a large number of tasks in mobile device,the disadvantages of limited energy and no power supply at the users are further exposed.MEC ultra-dense network system and task caching technology is combined in this thesis,so that a algorithm of task caching and offloading with Macro-Micro-base stations(TCOMM)is proposed firstly.The algorithm jointly designs the cache space scheduling,task cache and offload selection,and fine-grained division of task.The purpose is to reduce the energy consumption of the users based on the cache hit rate.And a mathematical optimization model using TCOMM is also established,the mixed integer nonlinear programming is solved by the proposed algorithm.Finally,the simulations show that the proposed energy efficiency optimization algorithm has a certain reduction in the energy consumption of the users,when the same task is completed which compare with the original TCO(Task Caching and Offloading)algorithm.Also,this algorithm can save energy compared with the most popular resource cache solution.