Research On Resource Allocation Strategy Based On Network Slicing In Heterogeneous Cellular Networks

With the continuous development of the global information society,the scale and use of mobile communication systems are changing dramatically.The next-generation wireless network will access various types of equipment,including smartphones,drones,and vehicles,which will place higher requirements on the performance of future cellular networks.In order to solve the increasing throughput demand,researchers have proposed Heterogeneous Cellular Network(HCN)technology.HCN can effectively improve the service quality of users at the edge of the macro cell,improve spectrum efficiency and system capacity,and achieve seamless access coverage.However,the heterogeneous cellular network contains a large number of different types of small base stations,which on the one hand intensifies energy consumption,and on the other hand,the resource management of the physical network is more complicated.At the same time,application scenarios where future cellular networks will face multiple business needs,so network slicing technology has emerged.Network slicing technology cuts multiple independent logical networks based on a single physical infrastructure to meet multiple business requirements while improving overall network efficiency and reducing management costs.However,due to the dynamic changes of wireless links,the application of slicing technology in cellular networks still faces challenges.Therefore,it is of great significance to study the combination of heterogeneous cellular networks and network slicing.This thesis mainly studies resource allocation strategies based on network slices in heterogeneous cellular network scenarios.Based on the needs of different network slices,the in-depth research is carried out with the goal of comprehensively considering the energy efficiency and spectrum efficiency of the network.The main contributions are as follows:First,for the downlink scenario of a two-layer heterogeneous cellular network,this thesis studies the energy efficiency optimization problem First,we build a two-layer HCN system model based on a Software Defined Network(SDN)controller enabled by Network Function Virtualization(NFV).Then we consider the two network slicing requirements of high-rate services and delay-sensitive services and the intra-layer interference of small base stations,and model the optimization problem as an Energy Efficiency(EE)maximization problem.Optimizing both bandwidth and power allocation in this EE problem is a Non-linear Fractional Programming(NFP)problem,and it is difficult to find the optimal solution.This paper proposes a two-layer nested loop algorithm.The outer layer uses Dinkelbach's method to transform the original problem into an equivalent problem and ensure the convergence of the overall algorithm.The inner-layer algorithm decomposes the equivalent problem into two sub-problems,and uses the interior point method and the Concave Convex Procedure(CCCP)algorithm to solve iteratively.Experimental simulations show that compared with existed allocation schemes,the joint optimization algorithm improves energy efficiency by 11%to 14%,and can effectively use network resources.Second,because Spectrum Efficiency(SE)and energy efficiency EE are key parameters of the cellular network,if you only pursue the maximization of a single parameter,it will inevitably lead to the sacrifice of another parameter.Based on the work in the first part,this thesis proposes the joint optimization problem of EE and SE in heterogeneous cellular networks.The system model still includes bandwidth,power allocation,and different quality of service(QoS)constraint requirements.In the modeling process,under the constraints of various constraints,we formed a multi-objective optimization problem(MOP)that jointly optimizes SE and EE.In order to obtain the Pareto optimal solution of this optimization problem,first,we convert the original problem into a single-objective optimization problem(SOP)through weighted sum method,and design a double layer similar to the first part Iterative algorithm solves the SOP problem.The outer layer uses the Dinkelbach algorithm to convert the SOP to a non-fractional objective function,and then the inner layer method is used to solve the two sub-problems.The loop is iterated until the algorithm converges.Finally,the simulation results verify that the resource allocation algorithm based on network slices can effectively achieve the compromise optimization of SE and EE.