Machine Learning-Based Self-Interference Cancellation for Full-Duplex Radio: Approaches, Open Challenges, and Future Research Directions

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...

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Bibliographic Details
Main Authors: Mohamed Elsayed, Ahmad A. Aziz El-Banna, Octavia A. Dobre, Wan Yi Shiu, Peiwei Wang
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Open Journal of Vehicular Technology
Subjects:
Artificial intelligence
deep learning (DL)
full-duplex (FD)
machine learning (ML)
neural networks (NNs)
self-interference cancellation (SIC)
Online Access:https://ieeexplore.ieee.org/document/10314438/

Internet

https://ieeexplore.ieee.org/document/10314438/

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