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

Traditional cellular networks have evolved from the first generation of analog communicationsto the current fourth generation of digital communications where iteratively enhanced physicallayer technologies have greatly increased the network capacity. According to Shannon’s theory,the technical gains brought by physical layer has gradually become saturated, which cannotmatch the rapid increase of user traffic demand in current mobile internet era, thus calls foranother path of evolution, i.e., digging into the traffic demand of mobile users. In recentyears, the academic communities have begun to use the real data to analyze the infrastructuredeployment of wireless networks and the traffic demand of mobile users, in order to makebenefits from the underlying statistical patterns. At the same time, along with the recent rise ofmachine 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 knowledgemining related services and telecommunication operators are coming to realize the increasingimportance of the recorded data from their own networks. Therefore, the real-data-driventechnology advancement is considered as a promising direction for the next evolution of cellularnetworks.Traditional cellular networks have evolved from the first generation of analog communicationsto the current fourth generation of digital communications where iteratively enhanced physicallayer technologies have greatly increased the network capacity. According to Shannon’s theory,the technical gains brought by physical layer has gradually become saturated, which cannotmatch the rapid increase of user traffic demand in current mobile internet era, thus calls foranother path of evolution, i.e., digging into the traffic demand of mobile users. In recentyears, the academic communities have begun to use the real data to analyze the infrastructuredeployment of wireless networks and the traffic demand of mobile users, in order to makebenefits from the underlying statistical patterns. At the same time, along with the recent rise ofmachine 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 knowledgemining related services and telecommunication operators are coming to realize the increasingimportance of the recorded data from their own networks. Therefore, the real-data-driventechnology advancement is considered as a promising direction for the next evolution of cellularnetworks