Machine Learning-Based Self-Interference Cancellation for Full-Duplex Radio: Approaches, Open Challenges, and Future Research Directions
In contrast to the long-held belief that wireless systems can only work in half-duplex mode, full-duplex (FD) systems are able to concurrently transmit and receive information over the same frequency bands to theoretically enable a twofold increase in spectral efficiency. Despite their significant p...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Vehicular Technology |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10314438/ |
| _version_ | 1827586121548169216 |
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| author | Mohamed Elsayed Ahmad A. Aziz El-Banna Octavia A. Dobre Wan Yi Shiu Peiwei Wang |
| author_facet | Mohamed Elsayed Ahmad A. Aziz El-Banna Octavia A. Dobre Wan Yi Shiu Peiwei Wang |
| author_sort | Mohamed Elsayed |
| collection | DOAJ |
| description | In contrast to the long-held belief that wireless systems can only work in half-duplex mode, full-duplex (FD) systems are able to concurrently transmit and receive information over the same frequency bands to theoretically enable a twofold increase in spectral efficiency. Despite their significant potential, FD systems suffer from an inherent self-interference (SI) due to a coupling of the transmit signal to its own FD receive chain. Self-interference cancellation (SIC) techniques are the key enablers for realizing the FD operation, and they could be implemented in the propagation, analog, and/or digital domains. Particularly, digital domain cancellation is typically performed using model-driven approaches, which have proven to be insufficient to seize the growing complexity of forthcoming communication systems. For the time being, machine learning (ML) data-driven approaches have been introduced for digital SIC to overcome the complexity hurdles of traditional methods. This article reviews and summarizes the recent advances in applying ML to SIC in FD systems. Further, it analyzes the performance of various ML approaches using different performance metrics, such as the achieved SIC, training overhead, memory storage, and computational complexity. Finally, this article discusses the challenges of applying ML-based techniques to SIC, highlights their potential solutions, and provides a guide for future research directions. |
| first_indexed | 2024-03-08T23:57:28Z |
| format | Article |
| id | doaj.art-264d1d2f789b4aa48f0b82a1f3bb8440 |
| institution | Directory Open Access Journal |
| issn | 2644-1330 |
| language | English |
| last_indexed | 2024-03-08T23:57:28Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj.art-264d1d2f789b4aa48f0b82a1f3bb84402023-12-13T00:02:33ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302024-01-015214710.1109/OJVT.2023.333118510314438Machine Learning-Based Self-Interference Cancellation for Full-Duplex Radio: Approaches, Open Challenges, and Future Research DirectionsMohamed Elsayed0https://orcid.org/0000-0001-8352-5264Ahmad A. Aziz El-Banna1https://orcid.org/0000-0001-7234-7642Octavia A. Dobre2https://orcid.org/0000-0001-8528-0512Wan Yi Shiu3https://orcid.org/0009-0003-4499-2515Peiwei Wang4https://orcid.org/0009-0008-3560-0037Faculty of Engineering and Applied Science, Memorial University, St. John's, NL, CanadaFaculty of Engineering and Applied Science, Memorial University, St. John's, NL, CanadaFaculty of Engineering and Applied Science, Memorial University, St. John's, NL, CanadaHuawei Technologies Canada Co., Ltd., Ottawa, ON, CanadaHuawei Technologies Canada Co., Ltd., Ottawa, ON, CanadaIn contrast to the long-held belief that wireless systems can only work in half-duplex mode, full-duplex (FD) systems are able to concurrently transmit and receive information over the same frequency bands to theoretically enable a twofold increase in spectral efficiency. Despite their significant potential, FD systems suffer from an inherent self-interference (SI) due to a coupling of the transmit signal to its own FD receive chain. Self-interference cancellation (SIC) techniques are the key enablers for realizing the FD operation, and they could be implemented in the propagation, analog, and/or digital domains. Particularly, digital domain cancellation is typically performed using model-driven approaches, which have proven to be insufficient to seize the growing complexity of forthcoming communication systems. For the time being, machine learning (ML) data-driven approaches have been introduced for digital SIC to overcome the complexity hurdles of traditional methods. This article reviews and summarizes the recent advances in applying ML to SIC in FD systems. Further, it analyzes the performance of various ML approaches using different performance metrics, such as the achieved SIC, training overhead, memory storage, and computational complexity. Finally, this article discusses the challenges of applying ML-based techniques to SIC, highlights their potential solutions, and provides a guide for future research directions.https://ieeexplore.ieee.org/document/10314438/Artificial intelligencedeep learning (DL)full-duplex (FD)machine learning (ML)neural networks (NNs)self-interference cancellation (SIC) |
| spellingShingle | Mohamed Elsayed Ahmad A. Aziz El-Banna Octavia A. Dobre Wan Yi Shiu Peiwei Wang Machine Learning-Based Self-Interference Cancellation for Full-Duplex Radio: Approaches, Open Challenges, and Future Research Directions IEEE Open Journal of Vehicular Technology Artificial intelligence deep learning (DL) full-duplex (FD) machine learning (ML) neural networks (NNs) self-interference cancellation (SIC) |
| title | Machine Learning-Based Self-Interference Cancellation for Full-Duplex Radio: Approaches, Open Challenges, and Future Research Directions |
| title_full | Machine Learning-Based Self-Interference Cancellation for Full-Duplex Radio: Approaches, Open Challenges, and Future Research Directions |
| title_fullStr | Machine Learning-Based Self-Interference Cancellation for Full-Duplex Radio: Approaches, Open Challenges, and Future Research Directions |
| title_full_unstemmed | Machine Learning-Based Self-Interference Cancellation for Full-Duplex Radio: Approaches, Open Challenges, and Future Research Directions |
| title_short | Machine Learning-Based Self-Interference Cancellation for Full-Duplex Radio: Approaches, Open Challenges, and Future Research Directions |
| title_sort | machine learning based self interference cancellation for full duplex radio approaches open challenges and future research directions |
| topic | Artificial intelligence deep learning (DL) full-duplex (FD) machine learning (ML) neural networks (NNs) self-interference cancellation (SIC) |
| url | https://ieeexplore.ieee.org/document/10314438/ |
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