The Role and Applications of Machine Learning in Future Self-Organizing Cellular Networks

In this chapter, a brief overview of the role and applications of machine learning (ML) algorithms in future wireless cellular networks is presented, more specifically, in the context of self-organizing networks (SONs). SON is a promising and innovative concept, in which future networks are expected to analyze and use historical data in order to improve and adapt themselves to the network conditions. For this to be possible, however, algorithms that are capable of extracting patterns from data and learn from previous actions are necessary. This chapter highlights the utilization and possible applications of ML algorithms in future cellular networks. A brief introduction of ML and SON is presented, followed by an analysis of current state of the art solutions involving ML in SON. Lastly, guidelines on the utilization of intelligent algorithms in SON and future research trends in the area are highlighted and conclusions are drawn. The Role and Applications of Machine Learning in Future Self-Organizing Cellular Networks

[1]  Haris Pervaiz,et al.  Analytical approach to base station sleep mode power consumption and sleep depth , 2017, 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[2]  Tapani Ristaniemi,et al.  Data mining framework for random access failure detection in LTE networks , 2014, 2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC).

[3]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[4]  Muhammad Ali Imran,et al.  Load Aware Self-Organising User-Centric Dynamic CoMP Clustering for 5G Networks , 2016, IEEE Access.

[5]  Muhammad Ali Imran,et al.  A Cell Outage Management Framework for Dense Heterogeneous Networks , 2016, IEEE Transactions on Vehicular Technology.

[6]  Stephan ten Brink,et al.  Deep Learning Based Communication Over the Air , 2017, IEEE Journal of Selected Topics in Signal Processing.

[7]  Muhammad Ali Imran,et al.  Self organising cloud cells: a resource efficient network densification strategy , 2015, Trans. Emerg. Telecommun. Technol..

[8]  Holger Claussen,et al.  Evolving femtocell coverage optimization algorithms using genetic programming , 2009, 2009 IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications.

[9]  Mehdi Bennis,et al.  Living on the edge: The role of proactive caching in 5G wireless networks , 2014, IEEE Communications Magazine.

[10]  Jeffrey G. Andrews,et al.  What Will 5G Be? , 2014, IEEE Journal on Selected Areas in Communications.

[11]  Andreas Mitschele-Thiel,et al.  Reinforcement learning strategies for self-organized coverage and capacity optimization , 2012, 2012 IEEE Wireless Communications and Networking Conference (WCNC).

[12]  Muhammad Ali Imran,et al.  Predictive and Core-Network Efficient RRC Signalling for Active State Handover in RANs With Control/Data Separation , 2017, IEEE Transactions on Wireless Communications.

[13]  Muhammad Ali Imran,et al.  Mobility prediction for handover management in cellular networks with control/data separation , 2015, 2015 IEEE International Conference on Communications (ICC).

[14]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

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

[16]  F. M. Landstorfer,et al.  Radio network planning with neural networks , 2000, Vehicular Technology Conference Fall 2000. IEEE VTS Fall VTC2000. 52nd Vehicular Technology Conference (Cat. No.00CH37152).

[17]  Muhammad Ali Imran,et al.  A Distributed SON-Based User-Centric Backhaul Provisioning Scheme , 2016, IEEE Access.

[18]  C.J. Debono,et al.  Cellular network coverage optimization through the application of self-organizing neural networks , 2005, VTC-2005-Fall. 2005 IEEE 62nd Vehicular Technology Conference, 2005..

[19]  Muhammad Ali Imran,et al.  An adaptive backhaul-aware cell range extension approach , 2015, 2015 IEEE International Conference on Communication Workshop (ICCW).

[20]  Zwi Altman,et al.  Self-organizing networks in next generation radio access networks: Application to fractional power control , 2011, Comput. Networks.

[21]  Ali Imran,et al.  Continuous Time Markov Chain Based Reliability Analysis for Future Cellular Networks , 2014, GLOBECOM 2014.

[22]  Qian Zhang,et al.  Transfer Learning Based Diagnosis for Configuration Troubleshooting in Self-Organizing Femtocell Networks , 2011, 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011.

[23]  Sotiris B. Kotsiantis,et al.  Supervised Machine Learning: A Review of Classification Techniques , 2007, Informatica.

[24]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[25]  Wei Su,et al.  Feature Space Analysis of Modulation Classification Using Very High-Order Statistics , 2013, IEEE Communications Letters.

[26]  Ian F. Akyildiz,et al.  Help from the Sky: Leveraging UAVs for Disaster Management , 2017, IEEE Pervasive Computing.

[27]  Muhammad Ali Imran,et al.  A Multiple Attribute User-Centric Backhaul Provisioning Scheme Using Distributed SON , 2016, 2016 IEEE Global Communications Conference (GLOBECOM).

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

[29]  Maaz Rehan,et al.  Effect of reinforcement learning on routing of cognitive radio ad-hoc networks , 2015, 2015 International Symposium on Mathematical Sciences and Computing Research (iSMSC).

[30]  Arsalan Darbandi,et al.  Enabling proactive self-healing by data mining network failure logs , 2017, 2017 International Conference on Computing, Networking and Communications (ICNC).

[31]  Richard Demo Souza,et al.  A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks , 2017, IEEE Communications Surveys & Tutorials.

[32]  James Irvine,et al.  An Advanced SOM Algorithm Applied to Handover Management Within LTE , 2013, IEEE Transactions on Vehicular Technology.

[33]  Rouzbeh Razavi,et al.  A Fuzzy reinforcement learning approach for self-optimization of coverage in LTE networks , 2010, Bell Labs Technical Journal.

[34]  Ang-Hsun Tsai,et al.  Bi-SON: Big-Data Self Organizing Network for Energy Efficient Ultra-Dense Small Cells , 2016, 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall).