Real-Time Overshoot and Undershoot Detection in Cellular Networks
One of the most crucial aspects of cellular networks is coverage, as it determines the areas where users can connect to the network and utilize its services. In the past, the use of planning tools was common practice for the establishment of coverage areas and network capacity prior to the deploymen...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10858714/ |
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| author | Jose Antonio Trujillo Rasmus Lykke Isabel de-la-Bandera Soren Sondergaard Troels B. Sonrensen Raquel Barco Preben E. Mogensen |
| author_facet | Jose Antonio Trujillo Rasmus Lykke Isabel de-la-Bandera Soren Sondergaard Troels B. Sonrensen Raquel Barco Preben E. Mogensen |
| author_sort | Jose Antonio Trujillo |
| collection | DOAJ |
| description | One of the most crucial aspects of cellular networks is coverage, as it determines the areas where users can connect to the network and utilize its services. In the past, the use of planning tools was common practice for the establishment of coverage areas and network capacity prior to the deployment of a network. However, issues with coverage, such as interference or coverage gaps, may arise due to equipment malfunctions, suboptimal configurations, or alterations in the propagation environment. In particular, an inadequate antenna tilt configuration can result in overshoot or undershoot situations on the network, which in turn can give rise to the aforementioned problems. This paper proposes a methodology for the real-time detection of overshoot and undershoot situations. To achieve this goal, KPI (Key Performance Indicators) are analyzed using machine learning techniques. Given the difficulty of detecting coverage problems in mobile networks, the results obtained suggest that the methodology provides a consistent knowledge base for optimizing the antenna tilt, thereby improving network performance. |
| format | Article |
| id | doaj-art-e5ffb8d56095418d8d426baae8aa3561 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e5ffb8d56095418d8d426baae8aa35612025-02-07T00:01:34ZengIEEEIEEE Access2169-35362025-01-0113223252234110.1109/ACCESS.2025.353732710858714Real-Time Overshoot and Undershoot Detection in Cellular NetworksJose Antonio Trujillo0https://orcid.org/0000-0003-2490-4875Rasmus Lykke1Isabel de-la-Bandera2https://orcid.org/0000-0003-4228-3494Soren Sondergaard3Troels B. Sonrensen4https://orcid.org/0000-0002-7940-7190Raquel Barco5https://orcid.org/0000-0002-8993-5229Preben E. Mogensen6https://orcid.org/0000-0002-0710-8685Telecommunication Research Institute (TELMA), E.T.S.I. de Telecomunicación, University of Malaga, Málaga, Spain2operate, Aalborg, DenmarkTelecommunication Research Institute (TELMA), E.T.S.I. de Telecomunicación, University of Malaga, Málaga, Spain2operate, Aalborg, Denmark2operate, Aalborg, DenmarkTelecommunication Research Institute (TELMA), E.T.S.I. de Telecomunicación, University of Malaga, Málaga, SpainDepartment of Electronic System, Wireless Communication Networks (WCN) Section, Aalborg University, Aalborg, DenmarkOne of the most crucial aspects of cellular networks is coverage, as it determines the areas where users can connect to the network and utilize its services. In the past, the use of planning tools was common practice for the establishment of coverage areas and network capacity prior to the deployment of a network. However, issues with coverage, such as interference or coverage gaps, may arise due to equipment malfunctions, suboptimal configurations, or alterations in the propagation environment. In particular, an inadequate antenna tilt configuration can result in overshoot or undershoot situations on the network, which in turn can give rise to the aforementioned problems. This paper proposes a methodology for the real-time detection of overshoot and undershoot situations. To achieve this goal, KPI (Key Performance Indicators) are analyzed using machine learning techniques. Given the difficulty of detecting coverage problems in mobile networks, the results obtained suggest that the methodology provides a consistent knowledge base for optimizing the antenna tilt, thereby improving network performance.https://ieeexplore.ieee.org/document/10858714/Cellular networkskey performance indicator (KPI)overshootundershootreal-timeself-organizing networks |
| spellingShingle | Jose Antonio Trujillo Rasmus Lykke Isabel de-la-Bandera Soren Sondergaard Troels B. Sonrensen Raquel Barco Preben E. Mogensen Real-Time Overshoot and Undershoot Detection in Cellular Networks IEEE Access Cellular networks key performance indicator (KPI) overshoot undershoot real-time self-organizing networks |
| title | Real-Time Overshoot and Undershoot Detection in Cellular Networks |
| title_full | Real-Time Overshoot and Undershoot Detection in Cellular Networks |
| title_fullStr | Real-Time Overshoot and Undershoot Detection in Cellular Networks |
| title_full_unstemmed | Real-Time Overshoot and Undershoot Detection in Cellular Networks |
| title_short | Real-Time Overshoot and Undershoot Detection in Cellular Networks |
| title_sort | real time overshoot and undershoot detection in cellular networks |
| topic | Cellular networks key performance indicator (KPI) overshoot undershoot real-time self-organizing networks |
| url | https://ieeexplore.ieee.org/document/10858714/ |
| work_keys_str_mv | AT joseantoniotrujillo realtimeovershootandundershootdetectionincellularnetworks AT rasmuslykke realtimeovershootandundershootdetectionincellularnetworks AT isabeldelabandera realtimeovershootandundershootdetectionincellularnetworks AT sorensondergaard realtimeovershootandundershootdetectionincellularnetworks AT troelsbsonrensen realtimeovershootandundershootdetectionincellularnetworks AT raquelbarco realtimeovershootandundershootdetectionincellularnetworks AT prebenemogensen realtimeovershootandundershootdetectionincellularnetworks |