Clustering Nature of Base Station and Traffic Demand in Cellular Networks and the Corresponding Caching and Multicast Strategies
Clustering Nature of Base Station and Traffic Demand in Cellular Networks and the Corresponding Caching and Multicast Strategies
Résumé
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.
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.
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