Big Data Analytics in Mobile Cellular Networks

Ying He; Fei Richard Yu; Nan Zhao; Hongxi Yin; Haipeng Yao; Robert C. Qiu

doi:10.1109/ACCESS.2016.2540520

IEEE Access (Jan 2016)

Big Data Analytics in Mobile Cellular Networks

Ying He,
Fei Richard Yu,
Nan Zhao,
Hongxi Yin,
Haipeng Yao,
Robert C. Qiu

Affiliations

Ying He: 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

DOI: https://doi.org/10.1109/ACCESS.2016.2540520
Journal volume & issue: Vol. 4
pp. 1985 – 1996

Abstract

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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.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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