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http://hdl.handle.net/10603/524013
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DC Field | Value | Language |
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dc.coverage.spatial | EC | |
dc.date.accessioned | 2023-11-07T11:11:56Z | - |
dc.date.available | 2023-11-07T11:11:56Z | - |
dc.identifier.uri | http://hdl.handle.net/10603/524013 | - |
dc.description.abstract | quotTitle: Crow Sunflower Optimization-based Handover Modules in 5G Networks newlineSubmitted By: Sanjay Sudhir Kulkarni newlineSupervised By: Dr. Arjav A. Bavarva newline newlineBackground: The handover techniques heavily rely on the Fifth Generation (5G) network. The deployment of tiny cells and overlay coverage make the 5G wireless network open, adaptable, and highly heterogeneous. One of the primary issues in the heterogeneous network is handover management. Additionally, handover serves the needs of highly stable and available 5G networks and reliable communications. The handover management handles every active connection for the user equipment. A connection between the user equipment and the relevant terminal is transferred from one network point to another using the handover method. The handover decision determines the best access network and whether the handover will occur. Due to the high signaling load resulting from the frequent handovers, network capability expansion may be hampered. The growing number of complex concerns and base stations, network management is challenging in this situation. newline newlineAim: To create a module to enable effective handover in a 5G network. The User Preference (UP) section, Network Quality of Service (NQ) module, Power Section, and Decision System (DS) module are the four primary components of this handover approach. The power module focused on electricity, and the UP section and NQ module regulated the Quality of service (QoS). As a result, three segments determine the handover, and the DS module enables the network. However, the DS module controls whether or not to give a handover in a 5G network. Additionally, using an optimization technique, the best decision is chosen. newlineMaterials and Methods: The Crow Sun Flower Optimization (CSFO) algorithm, a newly developed optimization technique, is intended for decision making and handover. The CSFO technique was created by fusing the Sun Flower Optimization (SFO) and Crow Search Algorithm (CSA) techniques. Additionally, three performance indicators, such as received | |
dc.format.extent | - | |
dc.language | English | |
dc.relation | No of References 69 | |
dc.rights | university | |
dc.title | Crow sun flower optimization based handover modules in 5g networks | |
dc.title.alternative | ||
dc.creator.researcher | Kulkarni, Sanjay.S. | |
dc.subject.keyword | 5G network | |
dc.subject.keyword | Decision module | |
dc.subject.keyword | Engineering | |
dc.subject.keyword | Engineering and Technology | |
dc.subject.keyword | Engineering Electrical and Electronic | |
dc.subject.keyword | Handover | |
dc.subject.keyword | Heterogeneous network | |
dc.description.note | References p. 51-58 | |
dc.contributor.guide | Bavarva, Arjav.A. | |
dc.publisher.place | Rajkot | |
dc.publisher.university | RK University | |
dc.publisher.institution | Faculty of Technology | |
dc.date.registered | 2020 | |
dc.date.completed | 2023 | |
dc.date.awarded | 2023 | |
dc.format.dimensions | - | |
dc.format.accompanyingmaterial | None | |
dc.source.university | University | |
dc.type.degree | Ph.D. | |
Appears in Departments: | Faculty of Technology |
Files in This Item:
File | Description | Size | Format | |
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01_title page.pdf | Attached File | 30.57 kB | Adobe PDF | View/Open |
02 preliminary pages.pdf | 530.96 kB | Adobe PDF | View/Open | |
03_content.pdf | 103.24 kB | Adobe PDF | View/Open | |
04_abstract & graphical abstract.pdf | 189.79 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 153.09 kB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 481.82 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 107.24 kB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 107.93 kB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 107.09 kB | Adobe PDF | View/Open | |
10_chapter 6.pdf | 523.38 kB | Adobe PDF | View/Open | |
11_chapter 7.pdf | 1.49 MB | Adobe PDF | View/Open | |
12_chapter 8.pdf | 269.2 kB | Adobe PDF | View/Open | |
13_chapter 9.pdf | 159.5 kB | Adobe PDF | View/Open | |
14_chapter 10.pdf | 91.56 kB | Adobe PDF | View/Open | |
15_annexures.pdf | 189.52 kB | Adobe PDF | View/Open | |
17_urkund report.pdf | 36.33 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 454.17 kB | Adobe PDF | View/Open |
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