Abstract
Future innovations rely on rapid technological advances to boost downlink strength. 5G ought to constitute one of the foundational technologies for an interconnected future. However, there is no contest that the end user's venturing style is committed to handover (HO) management. The common structure-based measurement of the target cell to clarify the handover processes requires a frequent measurement gap (MG), and these approaches must build a relationship to performance degradation. The deployment of ultra-dense small cells (UDSC) within a macro-cell layer contributes to multi-tier networks that are referred to as heterogeneous networks. (HetNets). The frequency of HOs and radio link failures (RLF) did, however, drastically grow along with the network's UDSC. As a result, in order to enhance the operation of the system, the management of mobility has grown to be a highly crucial task. Both the frequency of HOs and the percentage of HO failures (HOF) are objectives of the suggested approach. A simulation using 4G and 5G networks is carried out to determine how well the suggested technique performs. The simulation findings demonstrate that the suggested approach, when compared to existing algorithms from the literature, considerably lowers the typical levels of HO ping-pong (HOPP) and HOFs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Majid SI, Shah SW, Marwat SNK, Hafeez A, Ali H, Jan N (2021) Using an efficient technique based on dynamic learning period for improving delay in AI-based handover. Mob Inf Syst
Tan X, Chen G, Sun H (2020) Vertical handover algorithm based on multi-attribute and neural network in heterogeneous integrated network. EURASIP J Wirel Commun Network
Mollel MS, Abubakar AI, Ozturk M, Kaijage SF, Kisangiri M, Hussain S, Imran MA, Abbasi QH (2021) A survey of machine learning applications to handover management in 5G and beyond. IEEE Access
Tanveer J, Haider A, Ali R, Kim A (2022) An overview of reinforcement learning algorithms for handover management in 5G ultra-dense small cell networks. Appl Sci
Aljeri N, Boukerche A (2019) A two-tier machine learning-based handover management scheme for intelligent vehicular networks. Ad Hoc Netw
El Fachtali I, Saadane R, El Koutbi M (2017) Improved vertical handover decision algorithm using ants’ colonies with adaptive pheromone evaporation rate for 4th generation heterogeneous wireless networks. Int J Wirel Mob Comput 12(2)
Preethi GA, Gauthamarayathirumal P, Chandrasekar C (2019) Vertical handover analysis using modified MADM method in LTE. Mob Netw Appl
Mansouri M, Leghris C (2020) A use of fuzzy TOPSIS to improve the network selection in wireless multi access environments. J Comput Netw Commun
Ul Hasan N, Ejaz W, Ejaz N, Kim HS, Anpalagan A, Jo M (2016) Network selection and channel allocation for spectrum sharing in 5G heterogeneous networks. IEEE Access
Jia F, Zheng X (2018) A request-based handover strategy using NDN for 5G. Wirel Commun Mob Comput
Chamodrakas I, Martakos D (2011) A utility-based fuzzy TOPSIS method for energy efficient network selection in heterogeneous wireless networks. Appl Soft Comput
Basloom S, Akkari N, Aldabbagh G (2019) Reducing handoff delay in SDN-based 5G networks using AP clustering. In: Procedia computer science, 16th international learning and technology conference
Priya B, Malhotra J (2019) 5GAuNetS: an autonomous 5G network selection framework for Industry 4.0. Soft Comput
Priscoli FD, Giuseppi A, Liberati F, Pietrabissa A (2020) Traffic steering and network selection in 5G networks based on reinforcement learning. In: 2020 European control conference (ECC), 12–15 May 2020
Ai N, Wu B, Li B, Zhao Z (2021) 5G heterogeneous network selection and resource allocation optimization based on cuckoo search algorithm. Comput Commun
Wu Y, Zhao G, Ni D, Du J (2021) Dynamic handoff policy for RAN slicing by exploiting deep reinforcement learning. Eur J Wirel Commun Netw
Saad WK, Shayea I, Hamza BJ, Mohamad H, Daradkeh YI, Jabbar WA (2021) Handover parameters optimisation techniques in 5G networks. Sensors
Shayea I, Ergen M, Azmi MH, Çolak SA, Nordin R, Daradkeh YI (2020) Key challenges, drivers and solutions for mobility management in 5G networks: a survey. IEEE Access
Tayyab M, Gelabert X, Jäntti R (2019) A survey on handover management: from LTE to NR. IEEE Access
Ahmad R, Elankovan A, Sundararajan AK (2020) A survey on femtocell handover management in dense heterogeneous 5G networks. Telecommun Syst
Alhammadi A, Roslee M, Alias MY, Shayea I, Alquhali A (2020) Velocity-aware handover self-optimization management for next generation networks. Appl Sci
Alhammadi A, Roslee M, Alias MY, Shayea I, Alraih S, Mohamed KS (2019) Auto tuning self-optimization algorithm for mobility management in LTE-A and 5G HetNets. IEEE Access
Baghla S, Bansal S (2018) An approach to energy efficient vertical handover technique for heterogeneous networks. Int J Inf Technol
Singh P, Agrawal R (2019) AHP based network selection scheme for heterogeneous network in different traffic scenarios. Int J Inf Technol
Goutam S, Unnikrishnan S (2020) Algorithm for vertical handover in cellular networks using fuzzy logic. Int J Inf Technol
Nayakwadi N, Fatima R (2021) Automatic handover execution technique using machine learning algorithm for heterogeneous wireless networks. Int J Inf Technol
Dhand P, Mittal S, Sharma G (2021) An intelligent handoff optimization algorithm for network selection in heterogeneous networks. Int J Inf Technol
Acknowledgements
The first author sincerely acknowledges the financial support (University Research Fellowship) provided by the Department of Computer Science, Periyar University, under the grant: PU/AD-3/URF Selection Order/016175/2020.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Priyanka, A., Chandrasekar, C. (2023). Proactive Decision Making for Handover Management on Heterogeneous Networks. In: Ranganathan, G., Papakostas, G.A., Rocha, Á. (eds) Inventive Communication and Computational Technologies. ICICCT 2023. Lecture Notes in Networks and Systems, vol 757. Springer, Singapore. https://doi.org/10.1007/978-981-99-5166-6_43
Download citation
DOI: https://doi.org/10.1007/978-981-99-5166-6_43
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-5165-9
Online ISBN: 978-981-99-5166-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)