Machine learning algorithms in proactive decision making for handover management from 5G & beyond 5G
In recent years, heterogeneous networks (HetNets) have drawn a lot of attention to connecting devices that will enable everything to become smart, efficient, and fast. These networks are made up of many cell types, including macro, micro, pico, and femto that are added to suit customer demand. HetNe...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Egyptian Informatics Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866523000452 |
| _version_ | 1797730002755649536 |
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| author | A. Priyanka P. Gauthamarayathirumal C. Chandrasekar |
| author_facet | A. Priyanka P. Gauthamarayathirumal C. Chandrasekar |
| author_sort | A. Priyanka |
| collection | DOAJ |
| description | In recent years, heterogeneous networks (HetNets) have drawn a lot of attention to connecting devices that will enable everything to become smart, efficient, and fast. These networks are made up of many cell types, including macro, micro, pico, and femto that are added to suit customer demand. HetNets requires sophisticated mobility management to handle a variety of inter-frequency technologies. Mobility management needs to be adequately addressed to prevent service degradation caused by high rates of unnecessary handover attempts (HOA), handover ping-pong (HOPP), handover failure (HOF), radio link failure (RLF) and HO delay involved, which necessitates the user to execute the handover (HO) process while moving from one place to another. A well-suited HO management technique is proposed to resolve the issues observed when the user moves. The purpose of this study is to ascertain how the handover control parameter (HCP) involves the functionality of the 5G network. The novel approach taken into consideration in this work for cell selection is proactive decision-making (PDM). The performance of the proposed technique is evaluated through a simulation consisting of 5G Hetnets. Comparisons of evaluations were made in terms of HOA, HOPP, HOF, RLF and HO delay. |
| first_indexed | 2024-03-12T11:37:45Z |
| format | Article |
| id | doaj.art-c7169fb08cb5480baa8cc6e5f352a854 |
| institution | Directory Open Access Journal |
| issn | 1110-8665 |
| language | English |
| last_indexed | 2024-03-12T11:37:45Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj.art-c7169fb08cb5480baa8cc6e5f352a8542023-09-01T05:00:50ZengElsevierEgyptian Informatics Journal1110-86652023-09-01243100389Machine learning algorithms in proactive decision making for handover management from 5G & beyond 5GA. Priyanka0P. Gauthamarayathirumal1C. Chandrasekar2Department of Computer Science, Periyar University, Salem, Tamil Nadu, India; Corresponding author.Department of Computer Science, Government Arts College, Dharmapuri, Tamil Nadu, IndiaDepartment of Computer Science, Periyar University, Salem, Tamil Nadu, IndiaIn recent years, heterogeneous networks (HetNets) have drawn a lot of attention to connecting devices that will enable everything to become smart, efficient, and fast. These networks are made up of many cell types, including macro, micro, pico, and femto that are added to suit customer demand. HetNets requires sophisticated mobility management to handle a variety of inter-frequency technologies. Mobility management needs to be adequately addressed to prevent service degradation caused by high rates of unnecessary handover attempts (HOA), handover ping-pong (HOPP), handover failure (HOF), radio link failure (RLF) and HO delay involved, which necessitates the user to execute the handover (HO) process while moving from one place to another. A well-suited HO management technique is proposed to resolve the issues observed when the user moves. The purpose of this study is to ascertain how the handover control parameter (HCP) involves the functionality of the 5G network. The novel approach taken into consideration in this work for cell selection is proactive decision-making (PDM). The performance of the proposed technique is evaluated through a simulation consisting of 5G Hetnets. Comparisons of evaluations were made in terms of HOA, HOPP, HOF, RLF and HO delay.http://www.sciencedirect.com/science/article/pii/S1110866523000452Proactive decision making5GHandoverHandover control parameterMachine learning |
| spellingShingle | A. Priyanka P. Gauthamarayathirumal C. Chandrasekar Machine learning algorithms in proactive decision making for handover management from 5G & beyond 5G Egyptian Informatics Journal Proactive decision making 5G Handover Handover control parameter Machine learning |
| title | Machine learning algorithms in proactive decision making for handover management from 5G & beyond 5G |
| title_full | Machine learning algorithms in proactive decision making for handover management from 5G & beyond 5G |
| title_fullStr | Machine learning algorithms in proactive decision making for handover management from 5G & beyond 5G |
| title_full_unstemmed | Machine learning algorithms in proactive decision making for handover management from 5G & beyond 5G |
| title_short | Machine learning algorithms in proactive decision making for handover management from 5G & beyond 5G |
| title_sort | machine learning algorithms in proactive decision making for handover management from 5g amp beyond 5g |
| topic | Proactive decision making 5G Handover Handover control parameter Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S1110866523000452 |
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