Department of Information and Communication Engineering, Dalian University of Technology, Dalian, China
Fei Richard Yu
Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada
Nan Zhao
Department of Information and Communication Engineering, Dalian University of Technology, Dalian, China
Hongxi Yin
Department of Information and Communication Engineering, Dalian University of Technology, Dalian, China
Haipeng Yao
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China
Robert C. Qiu
Department of Electrical EngineeringResearch Center for Big Data Engineering and Technologies and the State Energy Smart Grid Research and Development Center, Shanghai Jiao Tong University, Shanghai, China
Mobile cellular networks have become both the generators and carriers of massive data. Big data analytics can improve the performance of mobile cellular networks and maximize the revenue of operators. In this paper, we introduce a unified data model based on the random matrix theory and machine learning. Then, we present an architectural framework for applying the big data analytics in the mobile cellular networks. Moreover, we describe several illustrative examples, including big signaling data, big traffic data, big location data, big radio waveforms data, and big heterogeneous data, in mobile cellular networks. Finally, we discuss a number of open research challenges of the big data analytics in the mobile cellular networks.
