Machine Learning (ML) In a 5G Standalone (SA) Self Organizing Network (SON)

© 2020 by IJCTT Journal
Volume-68 Issue-11
Year of Publication : 2020
Authors : Srinivasan Sridharan
DOI :  10.14445/22312803/IJCTT-V68I11P105

How to Cite?

Srinivasan Sridharan, "Machine Learning (ML) In a 5G Standalone (SA) Self Organizing Network (SON)," International Journal of Computer Trends and Technology, vol. 68, no. 11, pp. 43-48, 2020. Crossref, 10.14445/22312803/IJCTT-V68I11P105

Machine learning (ML) is included in Self-organizing Networks (SONs) that are key drivers for enhancing the Operations, Administration, and Maintenance (OAM) activities. It is included in the 5G Standalone (SA) system is one of the 5G communication tracks that transforms 4G networking to next-generation technology that is based on mobile applications. The research`s main aim is to an overview of machine learning (ML) in 5G standalone core networks. It was found that 5G intentions revere heterogeneous demands of phrases of data-rate, reliability, latency, or efficiency. Mobile operators shall be in a position in imitation of revere whole of these requirements using shared network infrastructure’s resources. 5G Standalone is considered a key enabler by the service providers as it improves the efficacy of the throughput that edges the network. It also assists in advancing new cellular use cases like ultra-reliable low latency communications (URLLC) that supports combinations of frequencies.

[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) (pp. 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 (2017) “Towards the fulfillment of 5G network requirements: technologies and challenges”. Telecommun Syst 65(1):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-2008 IEEE International Conference on Communications Workshops (pp. 37-41). IEEE.
[5] Andrews JG, Buzzi S, Choi W, Hanly SV, Lozano A, Soong AC, Zhang JC (2014) “What will 5G be?” IEEE J Sel Areas Commun 32(6):1065–1082
[6] Andrews, J. G., Buzzi, S., Choi, W., Hanly, S. V., Lozano, A., Soong, A. C., & Zhang, J. C. (2014). “What will 5G be?”. IEEE Journal on selected areas in communications, 32(6), 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 2016 International Symposium on Networks, Computers, and Communications (ISNCC) (pp. 1-6). IEEE.
[10] Foerster, J., Assael, I. A., De Freitas, N., & Whiteson, S. (2016). “Learning to communicate with deep multi-agent reinforcement learning”. In Advances in neural information processing systems (pp. 2137-2145).
[11] Hochreiter, S. JA1 4 rgen Schmidhuber (1997).“Long Short-Term Memory”. Neural Computation, 9(8).
[12] Hossain E, Hasan M (2015) “5G cellular: key enabling technologies and research challenges”. IEEE Instrum Meas Mag 18(3):11–21
[13] Imran, A., Zoha, A., & Abu-Dayya, A. (2014). “Challenges in 5G: how to empower SON with big data for enabling 5G”. IEEE Network, 28(6), 27-33.
[14] Jiang C, Zhang H, Ren Y, Han Z, Chen K-C, Hanzo L (2017) “Machine learning paradigms for next-generation wireless networks”. IEEE Wirel Commun 24(2):98–105
[15] 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.
[16] Lateef, H. Y., Imran, A., & Abu-Dayya, A. (2013, September). “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) (pp. 2898-2903). IEEE.
[17] Ma Z, Zhang Z, Ding Z, Fan P, Li H (2015) “Key techniques for 5G wireless communications: network architecture, physical layer, and mac layer perspectives”. Sci China Inf Sci 58(4):1–20
[18] 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), pp 3948–3953
[19] Mars, A., Abadleh, A., & Adi, W. (2019). “Operator and Manufacturer Independent D2D Private Link for Future 5G Networks.” arXiv preprintarXiv:1911.00303.
[20] Moysen, J., & Giupponi, L. (2018). “From 4G to 5G: Self-organized network management meets machine learning”. Computer Communications, 129, 248-268.
[21] Mumtaz, S., Huq, K. M. S., & Rodriguez, J. (2014). “Direct mobile-to-mobile communication: Paradigm for 5G”. IEEE Wireless Communications, 21(5), 14-23.
[22] Dr.V.V.Narendra Kumar, T.Satish Kumar, "Smarter Artificial Intelligence with Deep Learning" SSRG International Journal of Computer Science and Engineering 5.6 (2018): 10-16.
[23] 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
[24] Nishiyama, H., Kawamoto, Y., & Takaishi, D. (2017, September). “On OFDM-based resource allocation in LTE radio management system for uncrewed aerial vehicles (UAVs)”. In 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall) (pp. 1-5). IEEE.
[25] Zhang, Y., Yao, J., & Guan, H. (2017). “Intelligent cloud resource management with deep reinforcement learning”. IEEE Cloud Computing, 4(6), 60-69.
[26] Srinivasan Sridharan. (, 2020). “A Literature Review of Network Function Virtualization (NFV) in 5G Networks”. International Journal of Computer Trends and Technology, 68.10, 49-55.
[30] Srinivasan Sridharan. (, 2020). “5G Cloud Network Resource Slicing – A Literature Review”. International Research Journal of Engineering and Technology (IRJET) Volume 7, Issue 11, November 2020 S.No: 01
[35] O’Shea, T., & Hoydis, J. (2017). “An introduction to deep learning for the physical layer”. IEEE Transactions on Cognitive Communications and Networking, 3(4), 563-575.
[36] Perez, J. S., Jayaweera, S. K., & Lane, S. (2017, June). “Machine learning aided cognitive RAT selection for 5G heterogeneous networks”. In 2017 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom) (pp. 1-5). IEEE.
[37] Sultan K, Ali H, Zhang Z (2018) “Big data perspective and challenges in next-generation networks”. Future Internet 10(7):56
[38] Valente KP, Imran MA, Onireti O, Souza RD (2017) “A survey of machine learning techniques applied to self-organizing cellular networks”. IEEE Commun Surv Tutor 19:2392–2431
[39] Valente KP, Imran MA, Onireti O, Souza RD (2017) “A survey of machine learning techniques applied to self-organizing cellular networks”. IEEE Commun Surv Tutor 19:2392–2431
[40] Wei L, Hu RQ, Qian Y, Wu G (2014) “Enable device-to-device communications underlaying cellular networks: challenges and research aspects”. IEEE Commun Mag 52(6):90–96 [41] Xu, Y., Yin, F., Xu, W., Lin, J., & Cui, S. (2019). “Wireless traffic prediction with scalable Gaussian process: Framework, algorithms, and verification”. IEEE Journal on Selected Areas in Communications, 37(6), 1291-1306.

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