On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond Deployments
The need to optimize the deployment and maintenance costs for service delivery in wireless networks is an essential task for each service provider. The goal of this paper was to optimize the number of service centres (gNodeB) to cover selected customer locations based on the given requirements. This...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/24/8853 |
| _version_ | 1797545109359689728 |
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| author | Pavel Seda Milos Seda Jiri Hosek |
| author_facet | Pavel Seda Milos Seda Jiri Hosek |
| author_sort | Pavel Seda |
| collection | DOAJ |
| description | The need to optimize the deployment and maintenance costs for service delivery in wireless networks is an essential task for each service provider. The goal of this paper was to optimize the number of service centres (gNodeB) to cover selected customer locations based on the given requirements. This optimization need is especially emerging in emerging 5G and beyond cellular systems that are characterized by a large number of simultaneously connected devices, which is typically difficult to handle by the existing wireless systems. Currently, the network infrastructure planning tools used in the industry include Atoll Radio Planning Tool, RadioPlanner and others. These tools do not provide an automatic selection of a deployment position for specific gNodeB nodes in a given area with defined requirements. To design a network with those tools, a great deal of manual tasks that could be reduced by more sophisticated solutions are required. For that reason, our goal here and our main contribution of this paper were the development of new mathematical models that fit the currently emerging scenarios of wireless network deployment and maintenance. Next, we also provide the design and implementation of a verification methodology for these models through provided simulations. For the performance evaluation of the models, we utilize test datasets and discuss a case study scenario from a selected district in Central Europe. |
| first_indexed | 2024-03-10T14:10:46Z |
| format | Article |
| id | doaj.art-7a0623b129324bedbdbb5c6a01cd3012 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T14:10:46Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-7a0623b129324bedbdbb5c6a01cd30122023-11-21T00:14:24ZengMDPI AGApplied Sciences2076-34172020-12-011024885310.3390/app10248853On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond DeploymentsPavel Seda0Milos Seda1Jiri Hosek2Department of Telecommunications, Brno University of Technology, Technicka 12, 616 00 Brno, Czech RepublicInstitute of Automation and Computer Science, Brno University of Technology, Technicka 2, 616 69 Brno, Czech RepublicDepartment of Telecommunications, Brno University of Technology, Technicka 12, 616 00 Brno, Czech RepublicThe need to optimize the deployment and maintenance costs for service delivery in wireless networks is an essential task for each service provider. The goal of this paper was to optimize the number of service centres (gNodeB) to cover selected customer locations based on the given requirements. This optimization need is especially emerging in emerging 5G and beyond cellular systems that are characterized by a large number of simultaneously connected devices, which is typically difficult to handle by the existing wireless systems. Currently, the network infrastructure planning tools used in the industry include Atoll Radio Planning Tool, RadioPlanner and others. These tools do not provide an automatic selection of a deployment position for specific gNodeB nodes in a given area with defined requirements. To design a network with those tools, a great deal of manual tasks that could be reduced by more sophisticated solutions are required. For that reason, our goal here and our main contribution of this paper were the development of new mathematical models that fit the currently emerging scenarios of wireless network deployment and maintenance. Next, we also provide the design and implementation of a verification methodology for these models through provided simulations. For the performance evaluation of the models, we utilize test datasets and discuss a case study scenario from a selected district in Central Europe.https://www.mdpi.com/2076-3417/10/24/8853network coverage capacityoptimizationwireless networks5Gset covering problem |
| spellingShingle | Pavel Seda Milos Seda Jiri Hosek On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond Deployments Applied Sciences network coverage capacity optimization wireless networks 5G set covering problem |
| title | On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond Deployments |
| title_full | On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond Deployments |
| title_fullStr | On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond Deployments |
| title_full_unstemmed | On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond Deployments |
| title_short | On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond Deployments |
| title_sort | on mathematical modelling of automated coverage optimization in wireless 5g and beyond deployments |
| topic | network coverage capacity optimization wireless networks 5G set covering problem |
| url | https://www.mdpi.com/2076-3417/10/24/8853 |
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