Citation

Thillaigovindhan, Senthil Kumar and Roslee, Mardeni and Mitani, Sufian Mousa Ibrahim and Osman, Anwar Faizd and Ali, Fatimah Zaharah (2024) A Comprehensive Survey on Machine Learning Methods for Handover Optimization in 5G Networks. Electronics (Switzerland), 13 (16). p. 3223. ISSN 2079-9292

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Abstract

One of the key features of mobile networks in this age of mobile communication is seamless communication. Handover (HO) is a critical component of next-generation (NG) cellular communication networks, which requires careful management since it poses several risks to qualityof-service (QoS), including a decrease in average throughput and service disruptions. Due to the dramatic rise in base stations (BSs) and connections per unit area brought about by new fifthgeneration (5G) network enablers, such as Internet of things (IoT), network densification, and mmwave communications, HO management has become more challenging. The degree of difficulty is increased in light of the strict criteria that were recently published in the specifications of 5G networks. In order to address these issues more successfully and efficiently, this study has explored and examined intelligent HO optimization strategies using machine learning models. Furthermore, the significant goal of this review is to present the state of cellular networks as they are now, as well as to talk about mobility and home office administration in 5G alongside the overall features of 5G networks. This work presents an overview of machine learning methods in handover optimization and of the various data availability for evaluations. In the final section, the challenges and future research directions are also detailed.

Item Type:	Article
Uncontrolled Keywords:	handover; 5G networks; HO management; HO optimization; machine learning; challenges; research directions
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	01 Oct 2024 01:05
Last Modified:	01 Oct 2024 01:09
URII:	http://shdl.mmu.edu.my/id/eprint/12995

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