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Clustering Nature of Base Stations and Traffic Demands in Cellular Networks and the Corresponding Caching and Multicast Strategies

Abstract : 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. According to Shannon's theory, 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, thus calls for another path of evolution, i.e., digging into the traffic demand of mobile users. 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 measurements 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 assumption 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 phenomenon 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. According 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 accurately reproduce the practical BSs deployment, which leads to the spatial density characterization of BS.In Chapter 4, we tried 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 α-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 α-Stable distribution of temporally aggregated traffic volume on BS level.[...]
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Yifan Zhou. Clustering Nature of Base Stations and Traffic Demands in Cellular Networks and the Corresponding Caching and Multicast Strategies. Signal and Image processing. CentraleSupélec; Zhejiang University (Hangzhou, Chine), 2018. English. ⟨NNT : 2018CSUP0008⟩. ⟨tel-03102352⟩

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