Clustering Research in Fifth Generation Mobile Communication Networks

Year 2020, Volume: 5 Issue: 1, 25 - 31, 27.04.2020

Hasan Serdar

https://doi.org/10.30931/jetas.605244

Abstract

Clustering procedures are utilized to broaden the life of systems and increment vitality proficiency. In this study, some clustering algorithms and traffic expectations consider in 5G communication systems are inspected. There is a need to explain how to improve the nature of client experience through clustering. Understanding the requirements of clients is basic to give the capacity to help various situations in smart frameworks. Client mindfulness or client situated plan is a challenge in clustering. Inquires about have demonstrated that the usage of clustering plans in 5G systems presents difficulties and that clustering procedures created with insightful system determination arrangements can be of extraordinary advantage. The present investigations are not perfect in unique frameworks with a wide assortment of client situations since they are performed in both homogeneous and low-level heterogeneous systems and can't work. Also, when the 5G happens, the issue will turn out to be more mind-boggling than customarily rearranged. Different challenges identified with the execution of clustering methods in 5G networks are introduced and examined.

Keywords

5G, clustering, wireless networks, communications

References

• [1] Bruschi, R. et al., “A multi-clustering approach to scale distributed tenant networks for mobile edge computing”, IEEE Journal on Selected Areas in Communications, 37(3) (2019) : 499-514.
• [2] Le, L.-V. et al., “Applying big data, machine learning, and SDN/NFV to 5G traffic clustering, forecasting, and management” In 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft) (2018) : 168-176.
• [3] Sucasas, V. et al., “A survey on clustering techniques for cooperative wireless networks”. Ad Hoc Networks, 47 (2016) : 53-81.
• [4] Chun, Y.J. et al., “A comprehensive analysis of 5G heterogeneous cellular systems operating over shadowed fading channels”, IEEE Transactions on Wireless Communications, 16(11) (2017) : 6995-7010.
• [5] Ke, S. et al., “An adaptive clustering approach for small cell in ultra-dense networks”. In 2017 9th International Conference on Advanced Infocomm Technology (ICAIT) (2017) : 421-425.
• [6] Alawe, I. et al., “An efficient and lightweight load forecasting for proactive scaling in 5G mobile networks”, In 2018 IEEE Conference on Standards for Communications and Networking (CSCN) (2018) 1-6.
• [7] Paramonov, A. et al., “Clustering optimization for out-of-band d2d communications”, Wireless Communications and Mobile Computing, (2017).
• [8] Ouyang, Y. et al., “APP-SON: Application characteristics-driven SON to optimize 4G/5G network performance and quality of experience”, In 2017 IEEE International Conference on Big Data (Big Data) (2017) : 1514-1523.
• [9] Xu, L., O’Hare, G. & Collier, R., “A smart and balanced energy-efficient multihop clustering algorithm (smart-beem) for mimo iot systems in future networks”, Sensors, 17(7) (2017) : 1574.
• [10] Shariat, M. et al., “5G radio access above 6 GHz”, Transactions on Emerging Telecommunications Technologies, 27(9) (2016) : 1160-1167.
• [11] Xu, L., Collier, R. & O’Hare, G.M., “A survey of clustering techniques in WSNs and consideration of the challenges of applying such to 5G IoT scenarios”, IEEE Internet of Things Journal, 4(5) (2017) : 1229-1249.
• [12] Aadil, F. et al., “Clustering algorithm for internet of vehicles (IoV) based on dragonfly optimizer (CAVDO)”, The Journal of Supercomputing, 74(9) (2018) : 4542-4567.
• [13] Balevi, E. & Gitlin, R.D., “A clustering algorithm that maximizes throughput in 5G heterogeneous F-RAN networks”, In 2018 IEEE International Conference on Communications (ICC) (2018) : 1-6.
• [14] Chun, Y.J., Cotton, S.L., Dhillon, H.S., Ghrayeb, A., et al., “A stochastic geometric analysis of device-to-device communications operating over generalized fading channels”, IEEE Transactions on Wireless Communications, 16(7) (2017) : 4151-4165.
• [15] Feng, J. et al., “An approach to 5G wireless network virtualization: architecture and trial environment”, In 2017 IEEE Wireless Communications and Networking Conference (WCNC) (2017) : 1-6.
• [16] Feng, Z. et al., “An effective approach to 5G: Wireless network virtualization”, IEEE Communications Magazine, 53(12) (2015) : 53-59.
• [17] Ge, X. et al., “User mobility evaluation for 5G small cell networks based on individual mobility model”, IEEE Journal on Selected Areas in Communications, 34(3) ( 2016) : 528-541.
• [18] Gharbieh, M. et al., “Self-organized scheduling request for uplink 5G networks: A D2D clustering approach”, IEEE Transactions on Communications, 67(2) (2018) : 1197-1209.
• [19] Haneda, K. et al., “5G 3GPP-like channel models for outdoor urban microcellular and macrocellular environments”, In 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring) (2016) : -7.
• [20] He, R. et al., “An automatic clustering algorithm for multipath components based on Kernel-power-density”, In 2017 IEEE Wireless Communications and Networking Conference (WCNC) (2017) : 1-6.
• [21] Khan, Z. et al., “Two-level cluster based routing scheme for 5G V2X communication”, IEEE Access, 7 (2019) : 16194-16205.
• [22] Liu, Y. et al., “A cluster maintenance algorithm based on LEACH-DCHS protoclol”, In 2008 International Conference on Networking, Architecture, and Storage (2008) : 165-166.
• [23] Maatouk, A. et al., “Graph theory based approach to users grouping and downlink scheduling in FDD massive MIMO”, In 2018 IEEE International Conference on Communications (ICC), (2018) : 1-7.
• [24] Mathew, A.P., Arthi, M. & Babu, K.V., “An uniform clustering based coverage and cost effective placement of serving nodes for 5G”, In 2017 International Conference on Innovations in Electrical, Electronics, Instrumentation and Media Technology (ICEEIMT), (2017) : 106-110.
• [25] Moreno Garc’\ia-Loygorri, J. et al., “Wideband channel modeling for mm-wave inside trains for 5G-related applications”, Wireless Communications and Mobile Computing, (2018).
• [26] Pateromichelakis, E. et al., “Interference management enablers for 5g radio access networks”, In 2016 IEEE Conference on Standards for Communications and Networking (CSCN), (2016) : 1-7.
• [27] Ribeiro, F.C. et al., “Clustered multiuser detection for the uplink of 5G systems”, In 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring) (2016) : 1-5.
• [28] Saha, R.K., Nanba, S. & Nishimura, K., “A technique for cloud based clustering and spatial resource reuse and scheduling of 3D in-building small cells using CoMP for high capacity CRAN”, IEEE Access, 6 (2018) : 71602-71621.
• [29] Sangodoyin, S. et al., “Cluster characterization of 3-D MIMO propagation channel in an urban macrocellular environment”, IEEE Transactions on Wireless Communications, 17(8) (2018) : 5076-5091.
• [30] Wang, X. et al., “Clustering of virtual network function instances oriented to compatibility in 5G network”, China Communications, 14 (12) (2017) : 111-119.
• [31] Wang, Z., Qin, X. & Liu, B., “An energy-efficient clustering routing algorithm for WSN-assisted IoT”, In 2018 IEEE Wireless Communications and Networking Conference (WCNC) (2018) : 1-6.
• [32] Zhang, P. et al., “Cluster-based analysis of wideband millimeter-wave channel for corridor environment”, In 2017 Sixth Asia-Pacific Conference on Antennas and Propagation (APCAP) (2017) : 1-3.