DOI QR코드

DOI QR Code

Key Challenges of Mobility Management and Handover Process In 5G HetNets

  • Alotaibi, Sultan (College of Computer and Information Systems, Umm Al-Qura University)
  • Received : 2022.04.05
  • Published : 2022.04.30

Abstract

Wireless access technologies are emerging to enable high data rates for mobile users and novel applications that encompass both human and machine-type interactions. An essential approach to meet the rising demands on network capacity and offer high coverage for wireless users on upcoming fifth generation (5G) networks is heterogeneous networks (HetNets), which are generated by combining the installation of macro cells with a large number of densely distributed small cells Deployment in 5G architecture has several issues because to the rising complexity of network topology in 5G HetNets with many distinct base station types. Aside from the numerous benefits that dense small cell deployment delivers, it also introduces key mobility management issues such as frequent handover (HO), failures, delays and pingpong HO. This article investigates 5G HetNet mobility management in terms of radio resource control. This article also discusses the key challenges for 5G mobility management.

Keywords

References

  1. Zhang, H., Meng, N., Liu, Y., & Zhang, X. (2016). Performance evaluation for local anchor-based dual connectivity in 5G user-centric network. IEEE Access, 4, 5721-5729. https://doi.org/10.1109/ACCESS.2016.2606420
  2. Tufail, A., Namoun, A., Alrehaili, A., & Ali, A. (2021). A Survey on 5G Enabled Multi-Access Edge Computing for Smart Cities: Issues and Future Prospects. International Journal of Computer Science & Network Security, 21(6), 107-118. https://doi.org/10.22937/IJCSNS.2021.21.6.15
  3. Tiwari, R., & Deshmukh, S. (2019). MVU estimate of user velocity via gamma distributed handover count in HetNets. IEEE Communications Letters, 23(3), 482-485. https://doi.org/10.1109/lcomm.2019.2892962
  4. Hasan, M. M., Kwon, S., & Oh, S. (2018). Frequent-handover mitigation in ultra-dense heterogeneous networks. IEEE Transactions on Vehicular Technology, 68(1), 1035-1040. https://doi.org/10.1109/TVT.2018.2874692
  5. Xu, X., Tang, X., Sun, Z., Tao, X., & Zhang, P. (2019). Delay-oriented cross-tier handover optimization in ultradense heterogeneous networks. IEEE Access, 7, 21769- 21776. https://doi.org/10.1109/access.2019.2898430
  6. Zhang, Z., Junhui, Z., Ni, S., & Gong, Y. (2019). A seamless handover scheme with assisted eNB for 5G C/U plane split heterogeneous network. IEEE Access, 7, 164256-164264. https://doi.org/10.1109/access.2019.2952737
  7. Alhabo, M., Zhang, L., & Nawaz, N. (2019). GRA-based handover for dense small cells heterogeneous networks. IET Communications, 13(13), 1928-1935. https://doi.org/10.1049/iet-com.2018.5938
  8. Vasudeva, K., Simsek, M., Lopez-Perez, D., & Guvenc, I. (2015, June). Impact of channel fading on mobility management in heterogeneous networks. In 2015 IEEE International Conference on Communication Workshop (ICCW) (pp. 2206-2211). IEEE.
  9. Cacciapuoti, A. S. (2017). Mobility-aware user association for 5G mmWave networks. IEEE Access, 5, 21497-21507. https://doi.org/10.1109/ACCESS.2017.2751422
  10. Koda, Y., Nakashima, K., Yamamoto, K., Nishio, T., & Morikura, M. (2019). Handover management for mmwave networks with proactive performance prediction using camera images and deep reinforcement learning. IEEE Transactions on Cognitive Communications and Networking, 6(2), 802-816. https://doi.org/10.1109/tccn.2019.2961655
  11. Skrimponis, P., Dutta, S., Mezzavilla, M., Rangan, S., Mirfarshbafan, S. H., Studer, C., ... & Rodwell, M. (2020, March). Power consumption analysis for mobile mmWave and sub-THz receivers. In 2020 2nd 6G Wireless Summit (6G SUMMIT) (pp. 1-5). IEEE.
  12. Shayea, I., Abd. Rahman, T., Hadri Azmi, M., & Arsad, A. (2018). Rain attenuation of millimetre wave above 10 GHz for terrestrial links in tropical regions. Transactions on Emerging Telecommunications Technologies, 29(8), e3450. https://doi.org/10.1002/ett.3450
  13. Lu, J. S., Steinbach, D., Cabrol, P., & Pietraski, P. (2012). Modeling human blockers in millimeter wave radio links. ZTE communications, 10(4), 23-28.
  14. Giordani, M., Mezzavilla, M., Rangan, S., & Zorzi, M. (2016, June). Multi-connectivity in 5G mmWave cellular networks. In 2016 Mediterranean Ad Hoc Networking Workshop (Med- Hoc-Net) (pp. 1-7). IEEE.
  15. Polese, M., Giordani, M., Mezzavilla, M., Rangan, S., & Zorzi, M. (2017). Improved handover through dual connectivity in 5G mmWave mobile networks. IEEE Journal on Selected Areas in Communications, 35(9), 2069-2084. https://doi.org/10.1109/JSAC.2017.2720338
  16. Li, L., Wang, D., Niu, X., Chai, Y., Chen, L., He, L., ... & You, X. (2018). mmWave communications for 5G: implementation challenges and advances. Science China Information Sciences, 61(2), 1-19. https://doi.org/10.1007/s11432-017-9235-7
  17. Soleimani, H., Parada, R., Tomasin, S., & Zorzi, M. (2019). Fast initial access for mmWave 5G systems with hybrid beamforming using online statistics learning. IEEE Communications Magazine, 57(9), 132-137. https://doi.org/10.1109/mcom.2019.1800805
  18. Attaoui, W., Bouraqia, K., Sabir, E., Benjillali, M., & Elazouzi, R. (2019, June). Beam alignment game for selforganized mmWave-empowered 5G initial access. In 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC) (pp. 2050-2057). IEEE.
  19. Alkhateeb, A., Alex, S., Varkey, P., Li, Y., Qu, Q., & Tujkovic, D. (2018). Deep learning coordinated beamforming for highly-mobile millimeter wave systems. IEEE Access, 6, 37328-37348. https://doi.org/10.1109/access.2018.2850226
  20. Gures, E., Shayea, I., Alhammadi, A., Ergen, M., & Mohamad, H. (2020). A comprehensive survey on mobility management in 5G heterogeneous networks: Architectures, challenges and solutions. IEEE Access, 8, 195883-195913. https://doi.org/10.1109/access.2020.3030762
  21. Malm, N., Zhou, L., Menta, E., Ruttik, K., Jantti, R., Tirkkonen, O., ... & Leppanen, K. (2018, July). User localization enabled ultra-dense network testbed. In 2018 IEEE 5G World Forum (5GWF) (pp. 405-409). IEEE.
  22. Mohamed, A., Onireti, O., Imran, M. A., Imran, A., & Tafazolli, R. (2016). Predictive and core-network efficient RRC signalling for active state handover in RANs with control/data separation. IEEE Transactions on Wireless Communications, 16(3), 1423-1436. https://doi.org/10.1109/TWC.2016.2644608
  23. Huang, J., & Qian, Y. (2020). A secure and efficient handover authentication and key management protocol for 5G networks. Journal of Communications and Information Networks, 5(1), 40-49. https://doi.org/10.23919/JCIN.2020.9055109
  24. Zhang, Y., Deng, R. H., Bertino, E., & Zheng, D. (2019). Robust and universal seamless handover authentication in 5G HetNets. IEEE Transactions on Dependable and Secure Computing, 18(2), 858-874.
  25. Ma, R., Cao, J., Feng, D., Li, H., & He, S. (2019). FTGPHA: Fixed-trajectory group pre-handover authentication mechanism for mobile relays in 5G high-speed rail networks. IEEE transactions on vehicular technology, 69(2), 2126-2140. https://doi.org/10.1109/tvt.2019.2960313
  26. Alsaeedy, A. A., & Chong, E. K. (2019). Mobility management for 5G IoT devices: Improving power consumption with lightweight signaling overhead. IEEE Internet of Things Journal, 6(5), 8237-8247. https://doi.org/10.1109/jiot.2019.2920628
  27. Verbrugge, S., Pasqualini, S., Westphal, F. J., Jager, M., Iselt, A., Kirstadter, A., ... & Demeester, P. (2005, February). Modeling operational expenditures for telecom operators. In Proceedings of Conference on Optical Network Design and Modeling (pp. 455-466).
  28. Vasudeva, K., Dikmese, S., Guven, I., Mehbodniya, A., Saad, W., & Adachi, F. (2017). Fuzzy-based game theoretic mobility management for energy efficient operation in HetNets. IEEE Access, 5, 7542-7552. https://doi.org/10.1109/ACCESS.2017.2689061
  29. Zhang, B., Qi, W., & Zhang, J. (2018, January). An energy efficiency and ping-pong handover ratio optimization in twotier heterogeneous networks. In 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 532-536). IEEE.
  30. Zhang, J., Zeng, Y., & Zhang, R. (2017, May). Spectrum and energy efficiency maximization in UAV-enabled mobile relaying. In 2017 IEEE International Conference on Communications (ICC) (pp. 1-6). IEEE.
  31. Mukherjee, A. (2018). Energy efficiency and delay in 5G ultra-reliable low-latency communications system architectures. IEEE network, 32(2), 55-61. https://doi.org/10.1109/MNET.2018.1700260
  32. Hasan, M. M., & Kwon, S. (2019). Cluster-based load balancing algorithm for ultra-dense heterogeneous networks. IEEE Access, 8, 2153-2162. https://doi.org/10.1109/access.2019.2961949
  33. Han, P., Zhou, Z., & Wang, Z. (2020). User association for load balance in heterogeneous networks with limited CSI feedback. IEEE Communications Letters, 24(5), 1095-1099. https://doi.org/10.1109/lcomm.2020.2973090
  34. Addali, K. M., Melhem, S. Y. B., Khamayseh, Y., Zhang, Z., & Kadoch, M. (2019). Dynamic mobility load balancing for 5G small-cell networks based on utility functions. IEEE Access, 7, 126998-127011. https://doi.org/10.1109/access.2019.2939936
  35. Mohajer, A., Bavaghar, M., & Farrokhi, H. (2020). Mobilityaware load balancing for reliable self-organization networks: Multi-agent deep reinforcement learning. Reliability Engineering & System Safety, 202, 107056. https://doi.org/10.1016/j.ress.2020.107056
  36. Ma, B., Yang, B., Zhu, Y., & Zhang, J. (2020). Context-aware proactive 5G load balancing and optimization for urban areas. IEEE Access, 8, 8405-8417. https://doi.org/10.1109/access.2020.2964562
  37. Hu, J., Zhang, H., Liu, Y., Li, X., & Ji, H. (2019, April). An intelligent uav deployment scheme for load balance in small cell networks using machine learning. In 2019 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1-6). IEEE.