Space/Time Traffic Fluctuations in a Cellular Network: Measurements' Analysis and Potential Applications

The characterization of the space/time traffic profiles in a cellular network can be of high interest for automating the operation of future networks, since the knowledge extracted from the traffic fluctuations in a cell and its neighbours can be effectively exploited by different optimisation functions. In this context, this paper takes as an input a set of real traffic measurements in a cellular network deployed in a large city and analyses, on a per cell basis, the traffic profile characteristics at different time scales (week, day, hour). Then, the analysis is extended to the space dimension by considering the traffic of one cell in relation to that of its neighbours. This allows identifying traffic complementarities between neighbour cells at different time scales that can be exploited by certain optimisation functions, as illustrated in the paper with specific examples.

[1]  Xiaowei Qin,et al.  MSFS: Multiple Spatio-temporal Scales Traffic Forecasting in Mobile Cellular Network , 2016, 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech).

[2]  Yong Li,et al.  Big Data Driven Mobile Traffic Understanding and Forecasting: A Time Series Approach , 2016, IEEE Transactions on Services Computing.

[3]  O. Sallent,et al.  Artificial Intelligence-based 5G network capacity planning and operation , 2015, 2015 International Symposium on Wireless Communication Systems (ISWCS).

[4]  Juan Ramiro,et al.  Self-Organizing Networks (SON): Self-Planning, Self-Optimization and Self-Healing for GSM, UMTS and LTE , 2012 .

[5]  Honggang Zhang,et al.  Spatial modeling of the traffic density in cellular networks , 2014, IEEE Wireless Communications.

[6]  Song Chong,et al.  Traffic-Aware Energy-Saving Base Station Sleeping and Clustering in Cooperative Networks , 2018, IEEE Transactions on Wireless Communications.

[7]  Muhammad Ali Imran,et al.  Challenges in 5G: how to empower SON with big data for enabling 5G , 2014, IEEE Network.

[8]  Ji Ma,et al.  Modelling social characteristics of mobile radio networks , 2015, 2015 IEEE International Conference on Communication Workshop (ICCW).

[9]  Muhammad Ali Imran,et al.  A Survey of Self Organisation in Future Cellular Networks , 2013, IEEE Communications Surveys & Tutorials.

[10]  Hossam Afifi,et al.  Network planning tool based on network classification and load prediction , 2016, 2016 IEEE Wireless Communications and Networking Conference.

[11]  Xing Zhang,et al.  An Approach for Spatial-Temporal Traffic Modeling in Mobile Cellular Networks , 2015, 2015 27th International Teletraffic Congress.

[12]  Jacques Palicot,et al.  The prediction analysis of cellular radio access network traffic: From entropy theory to networking practice , 2014, IEEE Communications Magazine.

[13]  Shuangfeng Han,et al.  On Big Data Analytics for Greener and Softer RAN , 2015, IEEE Access.

[14]  Oriol Sallent,et al.  On Learning and Exploiting Time Domain Traffic Patterns in Cellular Radio Access Networks , 2016, MLDM.

[15]  Lusheng Ji,et al.  Geospatial and Temporal Dynamics of Application Usage in Cellular Data Networks , 2015, IEEE Transactions on Mobile Computing.