Clustering Nature Of Base Stations And Traffic Demands And The Corresponding Caching And Multicast Strategies

Traditional cellular networks have evolved from the first generation of analog communications to the current fourth generation of digital communications where iteratively enhanced physical layer technologies have greatly increased the network capacity.As the technical gains brought by physical layer has gradually become saturated,which cannot match the rapid increase of user traffic demand in current mobile internet era,another path of evolution is badly needed.In recent years,the academic communities have begun to use the real data to analyze the infrastructure deployment of wireless networks and the traffic demand of mobile users,in order to make benefits from the underlying statistical patterns.At the same time,along with the recent rise of machine learning technics,data-driven service is considered as the next economic growth point.Thus the industry is putting more and more attention on data accumulation and knowledge mining related services and telecommunication operators are coming to realize the increasing importance of the recorded data from their own networks.Therefore,the real-data-driven technology advancement is considered as a promising direction for the next evolution of cellular networks.In this thesis,we firstly gave a comprehensive review of the state-of-the-art real data mea-surements in Chapter 2 which not only sheds light on the importance of real data analysis,but also paves way for its reasonable usage to improve the service performance of cellular networks.From the survey,we concluded that there exhibits a periodic pattern for the temporal traffic assump-tion of large coverage area in cellular networks,while for single cell,a heavy-tailed distribution is widespread across the temporal and spatial characterization.Furthermore,this imbalance phe-nomenon emerges more significantly in the call duration,request arrivals and content preference of mobile users.Then,based on a large amount of real data collected from on-operating cellular networks,we conducted a large-scale identification on spatial modeling of base stations(BSs)in Chapter 3.Ac-cording to the fitting results,we verified the inaccuracy of Poisson distribution for BS locations,and uncovered the clustering nature of BS deployment in cellular networks.However,although typical clustering models have improved the modeling accuracy but are still not qualified to accu-rately reproduce the practical BSs deployment,which leads to the spatial density characterization of BS.In Chapter 4,we try to characterize the density of BS deployment and traffic demand,in both spatial domain and temporal dimensions.In accordance with the heavy-tailed phenomenons in Chapter 2,we found that the ?-Stable distribution is the most accurate model for the spatial densities of BSs and traffic consumption,between which a linear dependence is revealed through real data examination.Moreover,the accuracies of power-law and lognormal distributions for the packet length and inter-arrival time of user requests are verified,respectively,which convincingly leads to the ?-Stable distribution of temporally aggregated traffic volume on BS level.To make benefit from the findings in previous chapters,we proposed a cooperative caching strategy based on the spatial clustering of BSs and a dynamic unicast/multicast strategy based on the temporal aggregation of content requests in Chapter 5.According to the theoretical and simu-lation results,we found that the proposed 'Caching as a Cluster' strategy can significantly reduce the average delay of users especially in the inhomogeneous BS deployment scenario,and the dy-namic unicast/multicast strategy can not only reduce the average latency of content requests but also diminish the average power consumption of BSs especially under the bursty request arrival patterns.To implement the massive real data analyses and dynamic serving mechanisms aforemen-tioned,we proposed an intelligent SDN-based centralized architecture within cellular networks in Chapter 5.With the introduction of an intelligence center,the brand new architecture is able to trace the demand variations in real time,thus simultaneously satisfy the operational requirements of the entire network and QoEs of all users by deploying flexible and efficient algorithms upon it.Conclusively,in this thesis,we uncover the clustering nature of cellular networks in different dimensions,and proposed corresponding service strategies to tackle the clustering challenge and utilize them for efficiency improvement.