2024/10/01 by Gerald Enzner, Enzner, Gerald, Aleksej Chinaev +5 · 1 citation
Engineering · #Advanced Adaptive Filtering Techniques #FOS: Electrical engineering #Full-Duplex Wireless Communications #Signal Processing (eess.SP) #Ultra-Wideband Communications Technology #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2410.00894
openalex publication_date 2024/10/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.