Computational Problems Addressed using Machine Learning (ML) In a 5G Network
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© 2020 by IJCTT Journal
Volume-68 Issue-12
Year of Publication : 2020
Authors : Srinivasan Sridharan
DOI :  10.14445/22312803/IJCTT-V68I12P101

How to Cite?

Srinivasan Sridharan, "Computational Problems Addressed using Machine Learning (ML) In a 5G Network," International Journal of Computer Trends and Technology, vol. 68, no. 12, pp. 1-3, 2020. Crossref, 10.14445/22312803/IJCTT-V68I12P101

Abstract
Machine learning (ML) is one of the key drivers for a Self-organizing 5G Network (SONs), and it greatly improves Operational and Maintenance (OAM) activities such as Software/Hardware upgrades, Key Performance Indicator Monitoring, etc. This Case study paper specifically reviews the Computational problems which can be addressed by leveraging Machine learning techniques in a MIMO capable 5G Standalone network.

Keywords
5G, machine learning (ML), Self-organizing Networks (SONs), 5G Standalone, Artificial Intelligence (AI), MIMO.

Reference
[1] Alawe, I., Ksentini, A., Hadjadj-Aoul, Y., Bertin, P., & Kerbellec, A. (2017, September). On evaluating different trends for the virtualized and sdn-ready mobile network. In 2017 IEEE 6th International Conference on Cloud Networking (CloudNet) 1-6. IEEE.
[2] Aliu, O. G., Imran, A., Imran, M. A., & Evans, B. (2012). A survey of self-organization in future cellular networks. IEEE Communications Surveys & Tutorials, 15(1), 336-361.
[3] Alnoman A, Anpalagan A Towards the fulfillment of 5G network requirements: technologies and challenges. Telecommun Syst 65(1): (2017) 101–116.
[4] Amirijoo, M., Frenger, P., Gunnarsson, F., Kallin, H., Moe, J., & Zetterberg, K. (2008, May). Neighbor cell relation list and physical cell identity self-organization in LTE. In ICC Workshops- IEEE International Conference on Communications Workshops (2008) 37-41. IEEE.
[5] Andrews JG, Buzzi S, Choi W, Hanly SV, Lozano A, Soong AC, Zhang JC What will 5G be? IEEE J Sel Areas Commun 32(6): (2014) 1065–1082.
[6] Andrews, J. G., Buzzi, S., Choi, W., Hanly, S. V., Lozano, A., Soong, A. C., & Zhang, J. C.. What will 5G be?. IEEE Journal on selected areas in communications, 32(6), (2014) 1065-1082.
[7] Bashir, A. K., Arul, R., Basheer, S., Raja, G., Jayaraman, R., & Qureshi, N. M. F. (2019). An optimal multitier resource allocation of cloud RAN in 5G using machine learning—transactions on Emerging Telecommunications Technologies, 30(8), e3627.
[8] Bithas, P. S., Michailidis, E. T., Nomikos, N., Vouyioukas, D., & Kanatas, A. G. (2019). A survey on machine-learning techniques for UAV-based communications. Sensors, 19(23), 5170.
[9] Chabbouh, O., Rejeb, S. B., Choukair, Z., & Agoulmine, N. (2016, May). A novel cloud RAN architecture for 5G HetNets and QoS evaluation. In International Symposium on Networks, Computers, and Communications (ISNCC) 1-6. 2016. IEEE.
[10] Foerster, J., Assael, I. A., De Freitas, N., & Whiteson, S. Learning to communicate with deep multi-agent reinforcement learning. In Advances in neural information processing systems. 2016. 2137-2145.
[11] Sridharan, S. Systems and methods for authorization and billing of users for wireless charging. US Patent No.9,972,037. Washington, DC: US Patent and Trademark Office. (2018).
[12] Sridharan, S.(2016). Ue initiated an evolved packet core (epc) and ip multimedia subsystem (ims) network usage optimization algorithm for lte capable smartphones connected to wireless lan (Wi-Fi) network. US Patent No.20160088677A1. Washington, DC: US Patent and Trademark Office.
[13] Sridharan, S. Network initiated an evolved packet core (epc) and ip multimedia subsystem (ims) network usage optimization algorithm for lte capable smartphones connected to wireless lan (Wi-Fi) network. US Patent No.20160088678A1. Washington, DC: US Patent and Trademark Office. (2016).
[14] Hochreiter, S. JA1 4 rgen Schmidhuber.“Long Short-Term Memory”. Neural Computation, 9(8). (1997).
[15] Hossain E, Hasan M (2015) 5G cellular: key enabling technologies and research challenges. IEEE Instrum Meas Mag 18(3) 11–21.
[16] Imran, A., Zoha, A., & Abu-Dayya, A. Challenges in 5G: how to empower SON with big data for enabling 5G. IEEE Network, 28(6) (2014) 27-33.
[17] Jiang C, Zhang H, Ren Y, Han Z, Chen K-C, Hanzo L Machine learning paradigms for next-generation wireless networks. IEEE Wirel Commun 24(2) (2017) 98–105.
[18] Khorsandroo, S., Noor, R. M., & Khorsandroo, S. (2013). A generic quantitative relationship to assess the interdependency of QoE and QoS. KSII Transactions on Internet and Information Systems (TIIS), 7(2), 327-346.
[19] Lateef, H. Y., Imran, A., & Abu-Dayya, A. A framework for the classification of self-organizing network conflicts and coordination algorithms. In 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) 2898-2903. IEEE.
[20] Ma Z, Zhang Z, Ding Z, Fan P, Li H Key techniques for 5G wireless communications: network architecture, physical layer, and mac layer perspectives. Sci China Inf Sci 58(4) (2015) 1–20.
[21] MacCartney GR, Zhang J, Nie S, Rappaport TS (2013) Path loss models for 5G millimeter-wave propagation channels in urban microcells. In: 2013 IEEE global communications conference (GLOBECOM), 3948–3953.
[22] Mars, A., Abadleh, A., & Adi, W. Operator and Manufacturer Independent D2D Private Link for Future 5G Networks. arXiv preprintarXiv:1911.00303.https://arxiv.org/ftp/arxiv/papers/1911/1911.00303.pdf. 2019.
[23] Moysen, J., & Giupponi, L. From 4G to 5G: Self-organized network management meets machine learning. Computer Communications, 129, 2018.248-268.https://arxiv.org/pdf/1707.09300.pdf
[24] Mumtaz, S., Huq, K. M. S., & Rodriguez, J. (2014). Direct mobile-to-mobile communication: Paradigm for 5G. IEEE Wireless Communications, 21(5), 14-23.
[25] Nadeem Q-U-A, Kammoun A, Alouini M-S (2018) Elevation beamforming with full dimension mimo architectures in 5G systems: a tutorial. arXiv preprint arXiv:1805.00225
[26] Nishiyama, H., Kawamoto, Y., & Takaishi, D. On OFDM-based resource allocation in LTE radio management system for unmanned aerial vehicles (UAVs). In 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall) (pp. 1-5). IEEE.
[27] Zhang, Y., Yao, J., & Guan, H. Intelligent cloud resource management with deep reinforcement learning. IEEE Cloud Computing, 4(6) (2017). 60-69.
[28] Srinivasan Sridharan. A Literature Review of Network Function Virtualization (NFV) in 5G Networks. International Journal of Computer Trends and Technology, 68.10, (2020). 49-55.
[29] Srinivasan Sridharan. Machine Learning (ML) In a 5G Standalone (SA) Self Organizing Network (SON) International Journal of Computer Trends and Technology 68.11, (2020) 43-48.
[30] Srinivasan Sridharan. (, 2020). 5G Cloud Network Resource Slicing – A Literature Review. International Research Journal of Engineering and Technology (IRJET) 7(11), November 2020 https://irjet.net/volume7-issue11 S.No: 01
[31] O`Shea, T., & Hoydis, J. An introduction to deep learning for the physical layer. IEEE Transactions on Cognitive Communications and Networking, 3(4), (2017) 563-575.
[32] Srinivasan Sridharan. Network Function Virtualization (NFV) Design Considerations for a 5G Network Using Network Slicing Methodology. International Research Journal of Engineering and Technology (IRJET) 7(12) (2020).
[33] Perez, J. S., Jayaweera, S. K., & Lane, S. Machine learning aided cognitive RAT selection for 5G heterogeneous networks. In IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom) 1-5. 2017. IEEE.
[34] Sultan K, Ali H, Zhang Z Big data perspective and challenges in next-generation networks. Future Internet 10(7) (2018) 56.
[35] Valente KP, Imran MA, Onireti O, Souza RD A survey of machine learning techniques applied to self-organizing cellular networks. IEEE Commun Surv Tutor 19 (2017) 2392–2431.
[36] Valente KP, Imran MA, Onireti O, Souza RD A survey of machine learning techniques applied to self-organizing cellular networks. IEEE Commun Surv Tutor 19 (2017) 2392–2431.
[37] Wei L, Hu RQ, Qian Y, Wu G Enable device-to-device communications underlaying cellular networks: challenges and research aspects. IEEE Commun Mag 52(6) (2014) 90–96.
[38] Xu, Y., Yin, F., Xu, W., Lin, J., & Cui, S. Wireless traffic prediction with scalable Gaussian process: Framework, algorithms, and verification. IEEE Journal on Selected Areas in Communications, 37(6), (2019) 1291-1306.