Research On QoE-Oriented Hetnet Resource Managment Strategies And Algorithms

Recently, the demand of voice, video and data service is becoming higher with the rapid development of wireless communications. So that the frequency resource of LTE-Advanced system has been strained. It's difficult to meet the wireless communications coverage and capacity requirements simply by increasing the number of macro base stations. Therefore, Hetnet(Heterogeneous networks) has been formally proposed in LTE-Advanced standardization process. Hetnet can effectively improve the utilization of spectrum resources by deploying low-power nodes in traditional macro cell. The resource allocation problem about it is hot.In the past few years, most research work about Hetnet has focused on improving the cell capacity and system throughput. As the energy problem of equipment causes more and more attention, some researchers are working on the optimal allocation of resource based on energy efficiency. Although there are a lot of studies in this field, few of them consider how to allocate system resource based on QoE (Quality of Experience) and energy efficiency of different services in heterogeneous network. So this paper is working on the this problem.In this paper, we first investigate the resource allocation problem based on QoE of different services in Macro-Femto OFDMA downlink transmission network scene. In this scenario, we allocate wireless resource based on QoE model and propose an optimal resource algorithm. The algorithm which satisfies the QoE requirements of services makes the sum of total QoE maximized. The simulation results show that as the algorithm considers the QoE of heterogeneous services, it can significantly reduce the unnecessary waste of wireless resources and the user experience of system is superior to the traditional resource allocation algorithm based on the maximum throughput.The resource allocation in practical Hetnet should consider energy consumption as well as the user experience. So we studied the resource allocation problem to maximize energy efficiency based on QoE further. We use MOPSO(Multi Objective Particle Swarm Optimization) algorithm to solve this multi-objective optimization problem. Simulation results show that the algorithm can effectively avoid the phenomenon that energy waste on satisfied user equipment. Our research meets the principle of future green communications.