Artificial Intelligence for Radio Communication Context-Awareness
This paper surveys Artificial Intelligence (AI) methods for acquiring and managing context-of-operation awareness of radio communication nodes, links, and networks. The meaning and significance of context information and suitability of Machine Learning (ML) methods for the enrichment of context info...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9568860/ |
| _version_ | 1818741512626765824 |
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| author | Malgorzata Wasilewska Adrian Kliks Hanna Bogucka Krzysztof Cichon Julius Ruseckas Gediminas Molis Ausra Mackute-Varoneckiene Tomas Krilavicius |
| author_facet | Malgorzata Wasilewska Adrian Kliks Hanna Bogucka Krzysztof Cichon Julius Ruseckas Gediminas Molis Ausra Mackute-Varoneckiene Tomas Krilavicius |
| author_sort | Malgorzata Wasilewska |
| collection | DOAJ |
| description | This paper surveys Artificial Intelligence (AI) methods for acquiring and managing context-of-operation awareness of radio communication nodes, links, and networks. The meaning and significance of context information and suitability of Machine Learning (ML) methods for the enrichment of context information is discussed. A number of context features are considered in this regard and thorough analysis on which ML methods are suitable to which part of context learning is provided. The added value of the paper is the presentation of a synthesized framework of context-information processing, sharing, and management in a radio communication network by delineating a network-embedded subsystem for this management. Recommendations for a future AI/ML-based radio communication system architectures are also provided. |
| first_indexed | 2024-12-18T01:57:48Z |
| format | Article |
| id | doaj.art-6512135c7f3642a299841dc020671aab |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T01:57:48Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6512135c7f3642a299841dc020671aab2022-12-21T21:24:52ZengIEEEIEEE Access2169-35362021-01-01914482014485610.1109/ACCESS.2021.31195249568860Artificial Intelligence for Radio Communication Context-AwarenessMalgorzata Wasilewska0https://orcid.org/0000-0002-3471-0516Adrian Kliks1https://orcid.org/0000-0001-6766-7836Hanna Bogucka2https://orcid.org/0000-0002-1709-4862Krzysztof Cichon3Julius Ruseckas4Gediminas Molis5https://orcid.org/0000-0003-0914-2390Ausra Mackute-Varoneckiene6Tomas Krilavicius7Faculty of Computing and Telecommunications, Institute of Radiocommunications, Poznań University of Technology, Poznań, PolandFaculty of Computing and Telecommunications, Institute of Radiocommunications, Poznań University of Technology, Poznań, PolandFaculty of Computing and Telecommunications, Institute of Radiocommunications, Poznań University of Technology, Poznań, PolandFaculty of Computing and Telecommunications, Institute of Radiocommunications, Poznań University of Technology, Poznań, PolandBaltic Institute of Advanced Technology, Vilnius, LithuaniaBaltic Institute of Advanced Technology, Vilnius, LithuaniaBaltic Institute of Advanced Technology, Vilnius, LithuaniaBaltic Institute of Advanced Technology, Vilnius, LithuaniaThis paper surveys Artificial Intelligence (AI) methods for acquiring and managing context-of-operation awareness of radio communication nodes, links, and networks. The meaning and significance of context information and suitability of Machine Learning (ML) methods for the enrichment of context information is discussed. A number of context features are considered in this regard and thorough analysis on which ML methods are suitable to which part of context learning is provided. The added value of the paper is the presentation of a synthesized framework of context-information processing, sharing, and management in a radio communication network by delineating a network-embedded subsystem for this management. Recommendations for a future AI/ML-based radio communication system architectures are also provided.https://ieeexplore.ieee.org/document/9568860/Artificial intelligencecognitive radiocontext awarenessmachine learningradio context informationwireless communication |
| spellingShingle | Malgorzata Wasilewska Adrian Kliks Hanna Bogucka Krzysztof Cichon Julius Ruseckas Gediminas Molis Ausra Mackute-Varoneckiene Tomas Krilavicius Artificial Intelligence for Radio Communication Context-Awareness IEEE Access Artificial intelligence cognitive radio context awareness machine learning radio context information wireless communication |
| title | Artificial Intelligence for Radio Communication Context-Awareness |
| title_full | Artificial Intelligence for Radio Communication Context-Awareness |
| title_fullStr | Artificial Intelligence for Radio Communication Context-Awareness |
| title_full_unstemmed | Artificial Intelligence for Radio Communication Context-Awareness |
| title_short | Artificial Intelligence for Radio Communication Context-Awareness |
| title_sort | artificial intelligence for radio communication context awareness |
| topic | Artificial intelligence cognitive radio context awareness machine learning radio context information wireless communication |
| url | https://ieeexplore.ieee.org/document/9568860/ |
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