Capacity and Coverage Dimensioning for 5G Standalone Mixed-Cell Architecture: An Impact of Using Existing 4G Infrastructure
With the increasing demand for expected data volume daily, current telecommunications infrastructure can not meet requirements without using enhanced technologies adopted by 5G and beyond networks. Due to their diverse features, 5G technologies and services will be phenomenal in the coming days. Pro...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/16/11/423 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850227301299519488 |
|---|---|
| author | Naba Raj Khatiwoda Babu Ram Dawadi Sashidhar Ram Joshi |
| author_facet | Naba Raj Khatiwoda Babu Ram Dawadi Sashidhar Ram Joshi |
| author_sort | Naba Raj Khatiwoda |
| collection | DOAJ |
| description | With the increasing demand for expected data volume daily, current telecommunications infrastructure can not meet requirements without using enhanced technologies adopted by 5G and beyond networks. Due to their diverse features, 5G technologies and services will be phenomenal in the coming days. Proper planning procedures are to be adopted to provide cost-effective and quality telecommunication services. In this paper, we planned 5G network deployment in two frequency ranges, 3.5 GHz and 28 GHz, using a mixed cell structure. We used metaheuristic approaches such as Grey Wolf Optimization (GWO), Sparrow Search Algorithm (SSA), Whale Optimization Algorithm (WOA), Marine Predator Algorithm (MPA), Particle Swarm Optimization (PSO), and Ant Lion Optimization (ALO) for optimizing the locations of remote radio units. The comparative analysis of metaheuristic algorithms shows that the proposed network is efficient in providing an average data rate of 50 Mbps, can meet the coverage requirements of at least 98%, and meets quality-of-service requirements. We carried out the case study for an urban area and another suburban area of Kathmandu Valley, Nepal. We analyzed the outcomes of 5G greenfield deployment and 5G deployment using existing 4G infrastructure. Deploying 5G networks using existing 4G infrastructure, resources can be saved up to 33.7% and 54.2% in urban and suburban areas, respectively. |
| format | Article |
| id | doaj-art-e3a722bb7b5f4f2482494e4a8ab987a4 |
| institution | OA Journals |
| issn | 1999-5903 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-e3a722bb7b5f4f2482494e4a8ab987a42025-08-20T02:04:52ZengMDPI AGFuture Internet1999-59032024-11-01161142310.3390/fi16110423Capacity and Coverage Dimensioning for 5G Standalone Mixed-Cell Architecture: An Impact of Using Existing 4G InfrastructureNaba Raj Khatiwoda0Babu Ram Dawadi1Sashidhar Ram Joshi2Department of Electronics and Computer Engineering, Pulchowk Campus, Institute of Engineering, Tribhuvan University, Kathmandu 19758, NepalDepartment of Electronics and Computer Engineering, Pulchowk Campus, Institute of Engineering, Tribhuvan University, Kathmandu 19758, NepalDepartment of Electronics and Computer Engineering, Pulchowk Campus, Institute of Engineering, Tribhuvan University, Kathmandu 19758, NepalWith the increasing demand for expected data volume daily, current telecommunications infrastructure can not meet requirements without using enhanced technologies adopted by 5G and beyond networks. Due to their diverse features, 5G technologies and services will be phenomenal in the coming days. Proper planning procedures are to be adopted to provide cost-effective and quality telecommunication services. In this paper, we planned 5G network deployment in two frequency ranges, 3.5 GHz and 28 GHz, using a mixed cell structure. We used metaheuristic approaches such as Grey Wolf Optimization (GWO), Sparrow Search Algorithm (SSA), Whale Optimization Algorithm (WOA), Marine Predator Algorithm (MPA), Particle Swarm Optimization (PSO), and Ant Lion Optimization (ALO) for optimizing the locations of remote radio units. The comparative analysis of metaheuristic algorithms shows that the proposed network is efficient in providing an average data rate of 50 Mbps, can meet the coverage requirements of at least 98%, and meets quality-of-service requirements. We carried out the case study for an urban area and another suburban area of Kathmandu Valley, Nepal. We analyzed the outcomes of 5G greenfield deployment and 5G deployment using existing 4G infrastructure. Deploying 5G networks using existing 4G infrastructure, resources can be saved up to 33.7% and 54.2% in urban and suburban areas, respectively.https://www.mdpi.com/1999-5903/16/11/4235Gmixed radio cell structurefixed wireless accessmetaheuristic algorithmoptimization |
| spellingShingle | Naba Raj Khatiwoda Babu Ram Dawadi Sashidhar Ram Joshi Capacity and Coverage Dimensioning for 5G Standalone Mixed-Cell Architecture: An Impact of Using Existing 4G Infrastructure Future Internet 5G mixed radio cell structure fixed wireless access metaheuristic algorithm optimization |
| title | Capacity and Coverage Dimensioning for 5G Standalone Mixed-Cell Architecture: An Impact of Using Existing 4G Infrastructure |
| title_full | Capacity and Coverage Dimensioning for 5G Standalone Mixed-Cell Architecture: An Impact of Using Existing 4G Infrastructure |
| title_fullStr | Capacity and Coverage Dimensioning for 5G Standalone Mixed-Cell Architecture: An Impact of Using Existing 4G Infrastructure |
| title_full_unstemmed | Capacity and Coverage Dimensioning for 5G Standalone Mixed-Cell Architecture: An Impact of Using Existing 4G Infrastructure |
| title_short | Capacity and Coverage Dimensioning for 5G Standalone Mixed-Cell Architecture: An Impact of Using Existing 4G Infrastructure |
| title_sort | capacity and coverage dimensioning for 5g standalone mixed cell architecture an impact of using existing 4g infrastructure |
| topic | 5G mixed radio cell structure fixed wireless access metaheuristic algorithm optimization |
| url | https://www.mdpi.com/1999-5903/16/11/423 |
| work_keys_str_mv | AT nabarajkhatiwoda capacityandcoveragedimensioningfor5gstandalonemixedcellarchitectureanimpactofusingexisting4ginfrastructure AT baburamdawadi capacityandcoveragedimensioningfor5gstandalonemixedcellarchitectureanimpactofusingexisting4ginfrastructure AT sashidharramjoshi capacityandcoveragedimensioningfor5gstandalonemixedcellarchitectureanimpactofusingexisting4ginfrastructure |