• editor.aipublications@gmail.com
  • Track Your Paper
  • Contact Us
  • ISSN: 2456-2319

International Journal Of Electrical, Electronics And Computers(IJEEC)

Handover for 5G Networks using Fuzzy Logic: A Review

Kirandeep Kaur , Dr. Sonia Goyal , Dr. Amrit Kaur Bhullar

International Journal of Electrical, Electronics and Computers (IJECC), Vol-6,Issue-5, September - October 2021, Pages 25-33,

Download | Downloads : 2 | Total View : 250


The future organization world will be inserted with various ages of remote advances, like 4G and 5G. Simultaneously, the advancement of new gadgets outfitted with different interfaces is filling quickly as of late. As a result, the upward handover convention is created to give pervasive availability in the heterogeneous remote climate. Handover might be a fundamental a piece of any remote Mobile Communication Network. It is a way of mobile communication and portable communication during which cellular broadcast is relocate from one base station to another without losing connection to the mobile communication. Handover is one problem on Wireless Network (WN) and to unravel this problem various sorts of HO methods utilized in network. Fuzzy logic, Machine Learning and Optimization are the handover solving methods that are studied during this paper. This paper is a review of the handoff techniques. Fuzzy logic is that the best technique to unravel the HO problem and it's further implemented in 4G/5G network.

HetNets, self-optimization, handover, fuzzy logic, WSN, 4G and 5G.

[1] Ullah, R., Marwat, S. N. K., Ahmad, A. M., Ahmed, S., Hafeez, A., Kamal, T., & Tufail, M. (2020). A Machine Learning Approach for 5G SINR Prediction. Electronics, 9(10), 1660.
[2] Kumar, A. S., Vanmathi, S., Sanjay, B. P., Bharathi, S. R., & Meena, M. S. (2018). Handover forecasting in 5G using machine learning. International Journal of Engineering & Technology, 7(2.31), 76-79.
[3] Ali, Z., Baldo, N., Mangues-Bafalluy, J. and Giupponi, L., 2016, April. Machine learning based handover management for improved QoE in LTE. In NOMS 2016-2016 IEEE/IFIP Network Operations and Management Symposium, pp. 794-798.
[4] Santos, G. L., Endo, P. T., Sadok, D., & Kelner, J. (2020). When 5G meets deep learning: a systematic review. Algorithms, 13(9), 208.
[5] Sakthivel, B. (2021). Generic Framework For Handoff In Wireless Sensor Networks With Random Forest Classifier. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(9), 3117-3122.
[6] Aldossari, S., & Chen, K. C. (2019, November). Relay Selection for 5G New Radio Via Artificial Neural Networks. In 2019 22nd International Symposium on Wireless Personal Multimedia Communications (WPMC) ,pp. 1-5.
[7] Morocho-Cayamcela, M. E., Lee, H., & Lim, W. (2019). Machine learning for 5G/B5G mobile and wireless communications: Potential, limitations, and future directions. IEEE Access, 7, 137184-137206.
[8] Xie, F., Wei, D., & Wang, Z. (2021). Traffic analysis for 5G network slice based on machine learning. EURASIP Journal on Wireless Communications and Networking, 2021(1), 1-15.
[9] Alhammadi, A., Roslee, M., Alias, M. Y., Shayea, I., & Alquhali, A. (2020). Velocity-aware handover self-optimization management for next generation networks. Applied Sciences, 10(4), 1354.
[10] Alhammadi, A., Roslee, M., Alias, M.Y., Shayea, I., Alraih, S. and Mohamed, K.S., 2019. Auto tuning self-optimization algorithm for mobility management in LTE-A and 5G HetNets. IEEE Access, 8, pp.294-304.
[11] Lin, P. C., Casanova, L. F. G., & Fatty, B. K. (2016). Data-driven handover optimization in next generation mobile communication networks. Mobile Information Systems, 2016.
[12] Kiran, K., 2021. 5G heterogeneous network (HetNets): a self-optimization technique for vertical handover management. International Journal of Pervasive Computing and Communications.
[13] Beshley, M., Kryvinska, N., Yaremko, O. and Beshley, H., 2021. A Self-Optimizing Technique Based on Vertical Handover for Load Balancing in Heterogeneous Wireless Networks Using Big Data Analytics. Applied Sciences, 11(11), p.4737.
[14] Chandralekha, M. and Behera, P.K., 2011. Optimization of vertical handoff performance parameters in heterogeneous wireless networks. International Journal of Modern Engineering Research, 1(2), pp.597-601.
[15] Alhammadi, A., Roslee, M., Alias, M.Y., Shayea, I., Alriah, S. and Abas, A.B., 2019, July. Advanced handover self-optimization approach for 4G/5G HetNets using weighted fuzzy logic control. In 2019 15th International Conference on Telecommunications (ConTEL), pp. 1-6.
[16] Tanveer, J., Haider, A., Ali, R. and Kim, A., 2021. Reinforcement Learning-Based Optimization for Drone Mobility in 5G and Beyond Ultra-Dense Networks. Cmc-computers materials & continua, 68(3), pp.3807-3823.
[17] Monil, Mohammad AlaulHaque, RomasaQasim, and Rashedur M. Rahman. "Speed and direction based fuzzy handover system." In 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1-8.
[18] Alhammadi, A., Roslee, M., Alias, M.Y., Shayea, I. and Alquhali, A., 2020. Velocity-aware handover self-optimization management for next generation networks. Applied Sciences, 10(4), p.1354.
[19] Edwards, George, and Ravi Sankar. "Microcellular handoff using fuzzy techniques." Wireless Networks 4, no. 5 (1998): 401-409.
[20] Bchini, Tarek, Nabil Tabbane, Sami Tabbane, Emmanuel Chaput, and André-Luc Beylot. "Fuzzy logic based layers 2 and 3 handovers in IEEE 802.16 e network." Computer communications 33, no. 18 (2010): 2224-2245.
[21] Muñoz, P., Raquel Barco, and Isabel de la Bandera. "Load balancing and handover joint optimization in LTE networks using fuzzy logic and reinforcement learning." Computer Networks 76 (2015): 112-125.
[22] Kavitha, V., G. Manimala, and R. GokulKannan. "AI-Based Enhancement of Base Station Handover." Procedia Computer Science 165 (2019): 717-723.
[23] Kashmar, N., Atieh, M. and Haidar, A., 2016. Identifying the Effective Parameters for Vertical Handover in Cellular Networks Using Data Mining Techniques. Procedia Computer Science, 98, pp.91-99.
[24] Khalaf, G.A.F.M. and Badr, H.Z., 2013. A comprehensive approach to vertical handoff in heterogeneous wireless networks. Journal of King Saud University-Computer and Information Sciences, 25(2), pp.197-205.
[25] Jain, Aabha, and SanjivTokekar. "Application based vertical handoff decision in heterogeneous network." Procedia Computer Science 57 (2015): 782-788.
[26] Thumthawatworn, Thanachai. "Adaptive membership functions for handover decision system in wireless mobile network." Procedia Computer Science 86 (2016): 31-34.
[27] Abuhasnah, J.F. and Muradov, F.K., 2017. Direction prediction assisted handover using the multilayer perception neural network to reduce the handover time delays in LTE networks. Procedia computer science, 120, pp.719-727.
[28] Nie, S., Wu, D., Zhao, M., Gu, X., Zhang, L. and Lu, L., 2015. An enhanced mobility state estimation based handover optimization algorithm in LTE-A self-organizing network. Procedia Computer Science, 52, pp.270-277.