A Data-Driven Multiobjective Optimization Framework for Hyperdense 5G Network Planning
The trials and rollout of the fifth generation (5G) network technologies are gradually intensifying as 5G is positioned as a platform that not only accommodates exploding data traffic but also unlocks a multitude use cases, services and deployment scenarios. However, the need for hyperdense 5G deplo...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9194766/ |
| _version_ | 1818608533616197632 |
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| author | Beneyam Berehanu Haile Edward Mutafungwa Jyri Hamalainen |
| author_facet | Beneyam Berehanu Haile Edward Mutafungwa Jyri Hamalainen |
| author_sort | Beneyam Berehanu Haile |
| collection | DOAJ |
| description | The trials and rollout of the fifth generation (5G) network technologies are gradually intensifying as 5G is positioned as a platform that not only accommodates exploding data traffic but also unlocks a multitude use cases, services and deployment scenarios. However, the need for hyperdense 5G deployments is revealing some of the limitations of planning approaches that hitherto proved adequate for pre-5G systems. The hyperdensification envisioned in 5G networks not only adds complexity to network planning and optimization problems, but underlines need for more realistic data-driven approaches that consider cost, varying demands and other contextual attributes to produce feasible topologies. Furthermore, the quest for network programmability and automation including the 5G radio access network (RAN), as manifested by network slicing technologies and more flexible RAN architectures, are also among other factors that influence planning and optimization frameworks. Collectively, these deployment trends, technological developments and evolving (and diverse) service demands point towards the need for more holistic frameworks. This article proposes a data-driven multiobjective optimization framework for hyperdense 5G network planning with practical case studies used to illustrate added value compared to contemporary network planning and optimization approaches. Comparative results from the case study with real network data reveal potential performance and cost improvements of hyperdense optimized networks produced by the proposed framework due to increased use of contextual data of planning area and focus on objectives that target demand satisfaction. |
| first_indexed | 2024-12-16T14:44:10Z |
| format | Article |
| id | doaj.art-3214cb3805574c8bb7ecf314380156b4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T14:44:10Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3214cb3805574c8bb7ecf314380156b42022-12-21T22:27:49ZengIEEEIEEE Access2169-35362020-01-01816942316944310.1109/ACCESS.2020.30234529194766A Data-Driven Multiobjective Optimization Framework for Hyperdense 5G Network PlanningBeneyam Berehanu Haile0https://orcid.org/0000-0002-2148-1048Edward Mutafungwa1Jyri Hamalainen2https://orcid.org/0000-0002-3305-2961Department of Communications and Networking, School of Electrical Engineering, Aalto University, Espoo, FinlandDepartment of Communications and Networking, School of Electrical Engineering, Aalto University, Espoo, FinlandDepartment of Communications and Networking, School of Electrical Engineering, Aalto University, Espoo, FinlandThe trials and rollout of the fifth generation (5G) network technologies are gradually intensifying as 5G is positioned as a platform that not only accommodates exploding data traffic but also unlocks a multitude use cases, services and deployment scenarios. However, the need for hyperdense 5G deployments is revealing some of the limitations of planning approaches that hitherto proved adequate for pre-5G systems. The hyperdensification envisioned in 5G networks not only adds complexity to network planning and optimization problems, but underlines need for more realistic data-driven approaches that consider cost, varying demands and other contextual attributes to produce feasible topologies. Furthermore, the quest for network programmability and automation including the 5G radio access network (RAN), as manifested by network slicing technologies and more flexible RAN architectures, are also among other factors that influence planning and optimization frameworks. Collectively, these deployment trends, technological developments and evolving (and diverse) service demands point towards the need for more holistic frameworks. This article proposes a data-driven multiobjective optimization framework for hyperdense 5G network planning with practical case studies used to illustrate added value compared to contemporary network planning and optimization approaches. Comparative results from the case study with real network data reveal potential performance and cost improvements of hyperdense optimized networks produced by the proposed framework due to increased use of contextual data of planning area and focus on objectives that target demand satisfaction.https://ieeexplore.ieee.org/document/9194766/5Ghyperdense networksnetwork planningtechno-economicsnetwork data analyticsmultiobjective optimization |
| spellingShingle | Beneyam Berehanu Haile Edward Mutafungwa Jyri Hamalainen A Data-Driven Multiobjective Optimization Framework for Hyperdense 5G Network Planning IEEE Access 5G hyperdense networks network planning techno-economics network data analytics multiobjective optimization |
| title | A Data-Driven Multiobjective Optimization Framework for Hyperdense 5G Network Planning |
| title_full | A Data-Driven Multiobjective Optimization Framework for Hyperdense 5G Network Planning |
| title_fullStr | A Data-Driven Multiobjective Optimization Framework for Hyperdense 5G Network Planning |
| title_full_unstemmed | A Data-Driven Multiobjective Optimization Framework for Hyperdense 5G Network Planning |
| title_short | A Data-Driven Multiobjective Optimization Framework for Hyperdense 5G Network Planning |
| title_sort | data driven multiobjective optimization framework for hyperdense 5g network planning |
| topic | 5G hyperdense networks network planning techno-economics network data analytics multiobjective optimization |
| url | https://ieeexplore.ieee.org/document/9194766/ |
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