Large scale survey for radio propagation in developing machine learning model for path losses in communication systems
Several orthodox approaches, such as empirical methods and deterministic methods, had earlier been used for the prediction of path loss in wireless communication systems. These approaches are either inefficient or complex. Robustness and performance motivated the adoption of machine learning for mod...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | Scientific African |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468227623000091 |
| _version_ | 1811159636249673728 |
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| author | Haruna Chiroma Ponman Nickolas Nasir Faruk Emmanuel Alozie Imam-Fulani Yusuf Olayinka Kayode S. Adewole Abubakar Abdulkarim Abdulkarim A. Oloyede Olugbenga A. Sowande Salisu Garba Aliyu D. Usman Lawan S. Taura Yinusa A. Adediran |
| author_facet | Haruna Chiroma Ponman Nickolas Nasir Faruk Emmanuel Alozie Imam-Fulani Yusuf Olayinka Kayode S. Adewole Abubakar Abdulkarim Abdulkarim A. Oloyede Olugbenga A. Sowande Salisu Garba Aliyu D. Usman Lawan S. Taura Yinusa A. Adediran |
| author_sort | Haruna Chiroma |
| collection | DOAJ |
| description | Several orthodox approaches, such as empirical methods and deterministic methods, had earlier been used for the prediction of path loss in wireless communication systems. These approaches are either inefficient or complex. Robustness and performance motivated the adoption of machine learning for modeling path loss in wireless communication systems in place of traditional modeling schemes. Surveys on modeling path loss in communication systems exist in the literature; however, emerging deep learning architectures in-depth analysis, machine learning taxonomies related to path loss, and in-depth analysis on feature engineering in modeling path loss are missing in the already published surveys. To fill this existing gap, a survey of machine learning modeling for path loss in wireless communication systems is conducted to resolve the outlined issues, hence making this survey unique. Synthesis and analysis of deep learning architectures to solve path loss problems in communication systems are hereby presented. New taxonomy – deep learning, nature-inspired meta-heuristic algorithms, and shallow algorithms approach to path loss modeling have been created. Analysis of feature engineering in path loss modeling is exploited. Lastly, challenges militating against the full potential of modeling path loss based on machine learning are highlighted and discussed. Alternative approaches for resolving the outlined challenges are also presented in the survey to help in designing more practical applications in the future. |
| first_indexed | 2024-04-10T05:44:29Z |
| format | Article |
| id | doaj.art-a256b6e4b63b401f95f66fbca2423f37 |
| institution | Directory Open Access Journal |
| issn | 2468-2276 |
| language | English |
| last_indexed | 2024-04-10T05:44:29Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Scientific African |
| spelling | doaj.art-a256b6e4b63b401f95f66fbca2423f372023-03-06T04:17:59ZengElsevierScientific African2468-22762023-03-0119e01550Large scale survey for radio propagation in developing machine learning model for path losses in communication systemsHaruna Chiroma0Ponman Nickolas1Nasir Faruk2Emmanuel Alozie3Imam-Fulani Yusuf Olayinka4Kayode S. Adewole5Abubakar Abdulkarim6Abdulkarim A. Oloyede7Olugbenga A. Sowande8Salisu Garba9Aliyu D. Usman10Lawan S. Taura11Yinusa A. Adediran12Computer Science Department Federal College of Education (Technical) Gombe, Nigeria; College of Computer Science and Engineering, University of Hafr Batin, Hafr Batin, Saudi ArabiaDepartment of Mathematical Sciences, Abubakar Tafawa Balewa University, Bauchi, NigeriaDepartment of Information Technology, Sule Lamido University, Kafin Hausa, Jigawa State, Nigeria; Directorate of Information Technology, Sule Lamido University, Kafin Hausa, Jigawa State, Nigeria; Corresponding author at: Department of Information Technology, Sule Lamido University, Kafin Hausa, Jigawa State, Nigeria.Department of Telecommunication Science, University of Ilorin, NigeriaDepartment of Telecommunication Science, University of Ilorin, NigeriaDepartment of Computer Science, University of Ilorin, NigeriaDepartment of Electrical Engineering, Ahmadu Bello University, Zaria, NigeriaDepartment of Telecommunication Science, University of Ilorin, NigeriaDepartment of Telecommunication Science, University of Ilorin, NigeriaDepartment of Computer Science, Sule Lamido University, Kafin Hausa, Jigawa State, NigeriaDepartment of Electronics and Telecommunication Engineering, Ahmadu Bello University, Zaria, NigeriaDepartment of Physics, Sule Lamido University, Kafin Hausa, Jigawa State, NigeriaDepartment of Electrical and Electronics Engineering, University of Ilorin, NigeriaSeveral orthodox approaches, such as empirical methods and deterministic methods, had earlier been used for the prediction of path loss in wireless communication systems. These approaches are either inefficient or complex. Robustness and performance motivated the adoption of machine learning for modeling path loss in wireless communication systems in place of traditional modeling schemes. Surveys on modeling path loss in communication systems exist in the literature; however, emerging deep learning architectures in-depth analysis, machine learning taxonomies related to path loss, and in-depth analysis on feature engineering in modeling path loss are missing in the already published surveys. To fill this existing gap, a survey of machine learning modeling for path loss in wireless communication systems is conducted to resolve the outlined issues, hence making this survey unique. Synthesis and analysis of deep learning architectures to solve path loss problems in communication systems are hereby presented. New taxonomy – deep learning, nature-inspired meta-heuristic algorithms, and shallow algorithms approach to path loss modeling have been created. Analysis of feature engineering in path loss modeling is exploited. Lastly, challenges militating against the full potential of modeling path loss based on machine learning are highlighted and discussed. Alternative approaches for resolving the outlined challenges are also presented in the survey to help in designing more practical applications in the future.http://www.sciencedirect.com/science/article/pii/S2468227623000091Path loss modeling5G wireless communication systemsDeep learningArtificial neural networkFeature engineeringMachine learning |
| spellingShingle | Haruna Chiroma Ponman Nickolas Nasir Faruk Emmanuel Alozie Imam-Fulani Yusuf Olayinka Kayode S. Adewole Abubakar Abdulkarim Abdulkarim A. Oloyede Olugbenga A. Sowande Salisu Garba Aliyu D. Usman Lawan S. Taura Yinusa A. Adediran Large scale survey for radio propagation in developing machine learning model for path losses in communication systems Scientific African Path loss modeling 5G wireless communication systems Deep learning Artificial neural network Feature engineering Machine learning |
| title | Large scale survey for radio propagation in developing machine learning model for path losses in communication systems |
| title_full | Large scale survey for radio propagation in developing machine learning model for path losses in communication systems |
| title_fullStr | Large scale survey for radio propagation in developing machine learning model for path losses in communication systems |
| title_full_unstemmed | Large scale survey for radio propagation in developing machine learning model for path losses in communication systems |
| title_short | Large scale survey for radio propagation in developing machine learning model for path losses in communication systems |
| title_sort | large scale survey for radio propagation in developing machine learning model for path losses in communication systems |
| topic | Path loss modeling 5G wireless communication systems Deep learning Artificial neural network Feature engineering Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2468227623000091 |
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