Research On QoE-orientated Optimization Of Energy Efficiency In Next Generation Wireless Communication Systems

With the rapid development of ICT industry,green communication has become a hot topic in wireless systems.Users' experience,on the other hand,has also attracted much attention and grown into an important metric in evaluating the system's quality.Improving energy efficiency and valuing users'experience are the two key points in next-generation wireless systems.Though many attempts have been made to improve energy efficiency,few of them adopts QoE(Quality of Experience)as the evaluation criteria.This thesis focuses on the optimization of energy efficiency in wireless systems by combing the QoE with the base station sleeping strategy,aiming to achieve a good trade-off between QoE and energy efficiency.The thesis's work is as follows:First,the QoE mapping model of multi-applications is studied and a uniform mapping function has been proposed.As a subjective evaluation metric superior to objective QoS parameters,QoE,however,is difficult to quantize.QoE evaluation also varies along with different service types.This thesis proposes a uniform mapping model to integrate the QoE evaluation of the voice call service,file downloading service and video streaming service.Second,this thesis studies all the existing energy-saving solutions and realizes a typical base sleeping strategy.Base station sleeping,relay technique and cell zooming are the most popular energy saving methods.As a mature and widely-used technique,base station sleeping has been optimized in this thesis.This thesis also realizes a typical switching algorithm based on QoS parameters in single-service scenarios.Finally,this thesis combines QoE with the base station strategy and comes up with a dynamic switching algorithm in multi-service scenarios.By adopting Dynamic Programming algorithm,the proposed algorithm not only improves the energy efficiency in multi-service scenarios but also guarantees the QoE of users.The contributions of this thesis include 1)the uniform QoE mapping model for multi-services and 2)the dynamic programming base station sleeping algorithm which adopts QoE as the constraint and achieves the good trade-off between QoE and energy efficiency.