Social-Aware Forecasting for Cellular Networks Metrics

It has been long established that crowds generated by social events (e.g., sports matches, parades, fairs…) produce a high impact on cellular network service. However, to estimate such an impact, it is necessary to use data sources classically outside the mobile operator control. In this way, and following a social-aware approach, the forecasting mechanisms should be able to combine both social and network information to obtain reliable predictions. To this end, the present work develops a complete system for its use in the prediction of cellular metrics (e.g., connections, throughput…). The performance of the proposed solution is evaluated in a real cellular network, showing the capabilities of the approach to provide accurate forecasting.

[1]  Jiong Jin,et al.  Virtual Fog: A Virtualization Enabled Fog Computing Framework for Internet of Things , 2018, IEEE Internet of Things Journal.

[2]  Sergio Fortes Rodriguez,et al.  Management architecture for location-aware self-organizing LTE/LTE-a small cell networks , 2015, IEEE Communications Magazine.

[3]  Engin Zeydan,et al.  Detecting Mobile Traffic Anomalies Through Physical Control Channel Fingerprinting: A Deep Semi-Supervised Approach , 2019, IEEE Access.

[4]  Paolo Dini,et al.  Urban Anomaly Detection by processing Mobile Traffic Traces with LSTM Neural Networks , 2019, 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).

[5]  Nicola Bui,et al.  A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques , 2016, IEEE Communications Surveys & Tutorials.

[6]  Weisi Guo,et al.  A Survey of Online Data-Driven Proactive 5G Network Optimisation Using Machine Learning , 2020, IEEE Access.

[7]  Guilherme De A. Barreto,et al.  Long-term time series prediction with the NARX network: An empirical evaluation , 2008, Neurocomputing.

[8]  Gerhard Fettweis,et al.  Twitter as a Source for Spatial Traffic Information in Big Data-Enabled Self-Organizing Networks , 2017, 2017 IEEE Wireless Communications and Networking Conference (WCNC).

[9]  Peter Bühlmann,et al.  Bagging, Boosting and Ensemble Methods , 2012 .

[10]  Sergio Fortes,et al.  Estimation of Video Streaming KQIs for Radio Access Negotiation in Network Slicing Scenarios , 2020, IEEE Communications Letters.

[11]  Salvador Luna-Ramírez,et al.  A Context-Aware Data-Driven Algorithm for Small Cell Site Selection in Cellular Networks , 2020, IEEE Access.

[12]  Jianhua Li,et al.  Latency estimation for fog-based internet of things , 2017, 2017 27th International Telecommunication Networks and Applications Conference (ITNAC).

[14]  David Palacios,et al.  Applying Social Event Data for the Management of Cellular Networks , 2018, IEEE Communications Magazine.

[15]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..