ARENA: A Data-Driven Radio Access Networks Analysis of Football Events

Mass events represent one of the most challenging scenarios for mobile networks because, although their date and time are usually known in advance, the actual demand for resources is difficult to predict due to its dependency on many different factors. Based on data provided by a major European carrier during mass events in a football stadium comprising up to 30.000 people, 16 base station sectors and 1 Km2 area, we performed a data-driven analysis of the radio access network infrastructure dynamics during such events. Given the insights obtained from the analysis, we developed ARENA, a model-free deep learning Radio Access Network (RAN) capacity forecasting solution that, taking as input past network monitoring data and events context information, provides guidance to mobile operators on the expected RAN capacity needed during a future event. Our results, validated against real events contained in the dataset, illustrate the effectiveness of our proposed solution.

[1]  K. K. Ramakrishnan,et al.  Understanding the super-sized traffic of the super bowl , 2013, Internet Measurement Conference.

[2]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[3]  S. Vani,et al.  An Experimental Approach towards the Performance Assessment of Various Optimizers on Convolutional Neural Network , 2019, 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI).

[4]  Martin J. Wainwright,et al.  Optimal Rates for Zero-Order Convex Optimization: The Power of Two Function Evaluations , 2013, IEEE Transactions on Information Theory.

[5]  Adam Tauman Kalai,et al.  Online convex optimization in the bandit setting: gradient descent without a gradient , 2004, SODA '05.

[6]  Paul Patras,et al.  Long-Term Mobile Traffic Forecasting Using Deep Spatio-Temporal Neural Networks , 2017, MobiHoc.

[7]  Andres Garcia-Saavedra,et al.  Overbooking network slices through yield-driven end-to-end orchestration , 2018, CoNEXT.

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

[9]  Jing Wang,et al.  Spatiotemporal modeling and prediction in cellular networks: A big data enabled deep learning approach , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.

[10]  Kamran Sayrafian-Pour,et al.  Autonomous relocation strategies for cells on wheels in environments with prohibited areas , 2017, 2017 IEEE International Conference on Communications (ICC).

[11]  Kazuya Sakai,et al.  Data-Intensive Routing in Delay-Tolerant Networks , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.

[12]  Yunhao Liu,et al.  Spatio-temporal analysis and prediction of cellular traffic in metropolis , 2017, 2017 IEEE 25th International Conference on Network Protocols (ICNP).

[13]  Junbo Zhang,et al.  Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning , 2019, KDD.

[14]  Qing Ling,et al.  An Online Convex Optimization Approach to Proactive Network Resource Allocation , 2017, IEEE Transactions on Signal Processing.

[15]  Hari Krishna Vydana,et al.  Investigative study of various activation functions for speech recognition , 2017, 2017 Twenty-third National Conference on Communications (NCC).

[16]  Mounim A. El-Yacoubi,et al.  Estimation of Static and Dynamic Urban Populations with Mobile Network Metadata , 2018, IEEE Transactions on Mobile Computing.

[17]  Yike Guo,et al.  Deep Sequence Learning with Auxiliary Information for Traffic Prediction , 2018, KDD.

[18]  Yunhao Liu,et al.  Improving Urban Crowd Flow Prediction on Flexible Region Partition , 2020, IEEE Transactions on Mobile Computing.

[19]  Marco Ajmone Marsan,et al.  Why your smartphone doesn't work in very crowded environments , 2017, 2017 IEEE 18th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM).

[20]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[21]  Marco Fiore,et al.  DeepCog: Cognitive Network Management in Sliced 5G Networks with Deep Learning , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.

[22]  Xavier Costa-Perez,et al.  RL-NSB: Reinforcement Learning-Based 5G Network Slice Broker , 2019, IEEE/ACM Transactions on Networking.

[23]  Juha Kalliovaara,et al.  Detecting the impact of human mega-events on spectrum usage , 2016, 2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC).

[24]  Ozan Ozyegen,et al.  Experimental Results on the Impact of Memory in Neural Networks for Spectrum Prediction in Land Mobile Radio Bands , 2020, IEEE Transactions on Cognitive Communications and Networking.

[25]  Tommaso Melodia,et al.  The Slice Is Served: Enforcing Radio Access Network Slicing in Virtualized 5G Systems , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.

[26]  Shobha Venkataraman,et al.  A first look at cellular network performance during crowded events , 2013, SIGMETRICS '13.

[27]  Marco Fiore,et al.  How Should I Slice My Network?: A Multi-Service Empirical Evaluation of Resource Sharing Efficiency , 2018, MobiCom.

[28]  Dit-Yan Yeung,et al.  Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.

[29]  Fan Zhang,et al.  Exploring human mobility with multi-source data at extremely large metropolitan scales , 2014, MobiCom.

[30]  Marco Fiore,et al.  Not All Apps Are Created Equal: Analysis of Spatiotemporal Heterogeneity in Nationwide Mobile Service Usage , 2017, CoNEXT.

[31]  Matthew Chalmers,et al.  Visualisation of Spectator Activity at Stadium Events , 2009, 2009 13th International Conference Information Visualisation.

[32]  Stefania Sesia,et al.  LTE - The UMTS Long Term Evolution, Second Edition , 2011 .

[33]  Marco Ajmone Marsan,et al.  Speedtest-Like Measurements in 3G/4G Networks: The MONROE Experience , 2017, 2017 29th International Teletraffic Congress (ITC 29).

[34]  Yong Wang,et al.  Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction , 2017, Sensors.

[35]  Jörg Ott,et al.  Experience: Implications of Roaming in Europe , 2018, MobiCom.

[36]  Lujie Zhong,et al.  Optimal Information Centric Caching in 5G Device-to-Device Communications , 2018, IEEE Transactions on Mobile Computing.