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arXiv · 2410.00894

On Neural-Network Representation of Wireless Self-Interference for Inband Full-Duplex Communications

Gerald Enzner·Aleksej Chinaev·Svantje Voit·Aydin Sezgin
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Abstract

Neural network modeling is a key technology of science and research and a platform for deployment of algorithms to systems. In wireless communications, system modeling plays a pivotal role for interference cancellation with specifically high requirements of accuracy regarding the elimination of self-interference in full-duplex relays. This paper hence investigates the potential of identification and representation of the self-interference channel by neural network architectures. The approach is promising for its ability to cope with nonlinear representations, but the variability of channel characteristics is a first obstacle in straightforward application of data-driven neural networks. We therefore propose architectures with a touch of "adaptivity" to accomplish a successful training. For reproducibility of results and further investigations with possibly stronger models and enhanced performance, we document and share our data.

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Gerald Enzner, Aleksej Chinaev, Svantje Voit, Aydin Sezgin. 2024-10-01. On Neural-Network Representation of Wireless Self-Interference for Inband Full-Duplex Communications. https://arxiv.org/abs/2410.00894

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