Evaluation of 5G and fixed-satellite service earth station (FSS-ES) downlink interference based on artificial neural network learning models (ANN-LMS)
Fifth-generation (5G) networks have been deployed alongside fourth-generation networks in high-traffic areas. The most recent 5G mobile communication access technology includes mmWave and sub-6 GHz C-bands. However, 5G signals possibly interfere with existing radio systems because they are using adj...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
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Multidisciplinary Digital Publishing Institute
2023
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| Online Access: | http://psasir.upm.edu.my/id/eprint/107766/ |
| _version_ | 1848864984375754752 |
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| author | Al-Jumaily, Abdulmajeed Sali, Aduwati Jiménez, Víctor P. Gil Lagunas, Eva Natrah, Fatin Mohd Ikhsan Fontán, Fernando Pérez Hussein,, Yaseein Soubhi Singh, Mandeep Jit Samat, Fazdliana Aljumaily, Harith Al-Jumeily, Dhiya |
| author_facet | Al-Jumaily, Abdulmajeed Sali, Aduwati Jiménez, Víctor P. Gil Lagunas, Eva Natrah, Fatin Mohd Ikhsan Fontán, Fernando Pérez Hussein,, Yaseein Soubhi Singh, Mandeep Jit Samat, Fazdliana Aljumaily, Harith Al-Jumeily, Dhiya |
| author_sort | Al-Jumaily, Abdulmajeed |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Fifth-generation (5G) networks have been deployed alongside fourth-generation networks in high-traffic areas. The most recent 5G mobile communication access technology includes mmWave and sub-6 GHz C-bands. However, 5G signals possibly interfere with existing radio systems because they are using adjacent and co-channel frequencies. Therefore, the minimisation of the interference of 5G with other signals already deployed for other services, such as fixed-satellite service Earth stations (FSS-Ess), is urgently needed. The novelty of this paper is that it addresses issues using measurements from 5G base stations (5G-BS) and FSS-ES, simulation analysis, and prediction modelling based on artificial neural network learning models (ANN-LMs). The ANN-LMs models are used to classify interference events into two classes, namely, adjacent and co-channel interference. In particular, ANN-LMs incorporating the radial basis function neural network (RBFNN) and general regression neural network (GRNN) are implemented. Numerical results considering real measurements carried out in Malaysia show that RBFNN evidences better accuracy with respect to its GRNN counterpart. The outcomes of this work can be exploited in the future as a baseline for coexistence and/or mitigation techniques. |
| first_indexed | 2025-11-15T13:57:30Z |
| format | Article |
| id | upm-107766 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:57:30Z |
| publishDate | 2023 |
| publisher | Multidisciplinary Digital Publishing Institute |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1077662024-10-28T06:10:59Z http://psasir.upm.edu.my/id/eprint/107766/ Evaluation of 5G and fixed-satellite service earth station (FSS-ES) downlink interference based on artificial neural network learning models (ANN-LMS) Al-Jumaily, Abdulmajeed Sali, Aduwati Jiménez, Víctor P. Gil Lagunas, Eva Natrah, Fatin Mohd Ikhsan Fontán, Fernando Pérez Hussein,, Yaseein Soubhi Singh, Mandeep Jit Samat, Fazdliana Aljumaily, Harith Al-Jumeily, Dhiya Fifth-generation (5G) networks have been deployed alongside fourth-generation networks in high-traffic areas. The most recent 5G mobile communication access technology includes mmWave and sub-6 GHz C-bands. However, 5G signals possibly interfere with existing radio systems because they are using adjacent and co-channel frequencies. Therefore, the minimisation of the interference of 5G with other signals already deployed for other services, such as fixed-satellite service Earth stations (FSS-Ess), is urgently needed. The novelty of this paper is that it addresses issues using measurements from 5G base stations (5G-BS) and FSS-ES, simulation analysis, and prediction modelling based on artificial neural network learning models (ANN-LMs). The ANN-LMs models are used to classify interference events into two classes, namely, adjacent and co-channel interference. In particular, ANN-LMs incorporating the radial basis function neural network (RBFNN) and general regression neural network (GRNN) are implemented. Numerical results considering real measurements carried out in Malaysia show that RBFNN evidences better accuracy with respect to its GRNN counterpart. The outcomes of this work can be exploited in the future as a baseline for coexistence and/or mitigation techniques. Multidisciplinary Digital Publishing Institute 2023-07-05 Article PeerReviewed Al-Jumaily, Abdulmajeed and Sali, Aduwati and Jiménez, Víctor P. Gil and Lagunas, Eva and Natrah, Fatin Mohd Ikhsan and Fontán, Fernando Pérez and Hussein,, Yaseein Soubhi and Singh, Mandeep Jit and Samat, Fazdliana and Aljumaily, Harith and Al-Jumeily, Dhiya (2023) Evaluation of 5G and fixed-satellite service earth station (FSS-ES) downlink interference based on artificial neural network learning models (ANN-LMS). Sensors, 23 (13). pp. 1-32. ISSN 1424-8239; eISSN: 1424-8220 https://www.mdpi.com/1424-8220/23/13/6175 10.3390/s23136175 |
| spellingShingle | Al-Jumaily, Abdulmajeed Sali, Aduwati Jiménez, Víctor P. Gil Lagunas, Eva Natrah, Fatin Mohd Ikhsan Fontán, Fernando Pérez Hussein,, Yaseein Soubhi Singh, Mandeep Jit Samat, Fazdliana Aljumaily, Harith Al-Jumeily, Dhiya Evaluation of 5G and fixed-satellite service earth station (FSS-ES) downlink interference based on artificial neural network learning models (ANN-LMS) |
| title | Evaluation of 5G and fixed-satellite service earth station (FSS-ES) downlink interference based on artificial neural network learning models (ANN-LMS) |
| title_full | Evaluation of 5G and fixed-satellite service earth station (FSS-ES) downlink interference based on artificial neural network learning models (ANN-LMS) |
| title_fullStr | Evaluation of 5G and fixed-satellite service earth station (FSS-ES) downlink interference based on artificial neural network learning models (ANN-LMS) |
| title_full_unstemmed | Evaluation of 5G and fixed-satellite service earth station (FSS-ES) downlink interference based on artificial neural network learning models (ANN-LMS) |
| title_short | Evaluation of 5G and fixed-satellite service earth station (FSS-ES) downlink interference based on artificial neural network learning models (ANN-LMS) |
| title_sort | evaluation of 5g and fixed-satellite service earth station (fss-es) downlink interference based on artificial neural network learning models (ann-lms) |
| url | http://psasir.upm.edu.my/id/eprint/107766/ http://psasir.upm.edu.my/id/eprint/107766/ http://psasir.upm.edu.my/id/eprint/107766/ |