CNN-aided self-interference estimation for in-band full-duplex systems
Modern radio access technologies approach Shannon’s limit, necessitating innovative methods for enhanced spectral efficiency. In-band full-duplex (IBFD) can double the spectral efficiency, enabling simultaneous transmission and reception over the same time-frequency resource. IBFD faces the challeng...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad del País Vasco |
| Repositorio: | Addi. Archivo Digital para la Docencia y la Investigación |
| OAI Identifier: | oai:addi.ehu.eus:10810/73907 |
| Acceso en línea: | http://hdl.handle.net/10810/73907 |
| Access Level: | acceso abierto |
| Palabra clave: | DL channel estimation CNN full-duplex IBFD loopback cancellation |
| Sumario: | Modern radio access technologies approach Shannon’s limit, necessitating innovative methods for enhanced spectral efficiency. In-band full-duplex (IBFD) can double the spectral efficiency, enabling simultaneous transmission and reception over the same time-frequency resource. IBFD faces the challenge of self-interference, which has to be canceled by up to 100 dB. This paper estimates the loopback channel through convolutional neural networks (CNNs), which leverage the natural signal structure of wireless channels, effectively mapping time-frequency features. We test the method via simulations in two measured channels, showing cancellations of up to 52 dB. |
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