In frequency division duplex systems with simultaneous transmission and reception, receivers suffer from outof- band emissions by the wireless transmitter leaking into the receive band. This so-called self-interference can be cancelled by means of estimating the interfering signal from the known transmit signal and subtracting it from the received signal. Conventional cancellation approaches rely on a mathematical model of the self-interference generation. However, these cannot capture effects beyond their model assumptions. With neural networkbased cancellation, present interference-generating effects are captured during data-driven training. In this work, we combine such a cancellation method with a neural network-based receiver for the demodulation of the signal-of-interest. Simulation results with 5G NR-conform waveforms show that this approach is both well-performing and adaptive to many self-interference and radio channel scenarios. Our approach is able to cancel selfinterference that has undergone power amplifier nonlinearity, IQ imbalance, and a frequency-selective channel down to a signalto- interference ratio of 0.0 dB.
Description
Neural Network-Based Self-Interference Cancellation for Frequency Division Duplex | VDE Conference Publication | IEEE Xplore
%0 Conference Paper
%1 10925912
%A Beckworth, Mike
%A Fuchs, Ephraim
%A Fischer, Moritz Benedikt
%A ten Brink, Stephan
%B European Wireless 2024; 29th European Wireless Conference
%D 2024
%K myown
%P 47-52
%T Neural Network-Based Self-Interference Cancellation for Frequency Division Duplex
%X In frequency division duplex systems with simultaneous transmission and reception, receivers suffer from outof- band emissions by the wireless transmitter leaking into the receive band. This so-called self-interference can be cancelled by means of estimating the interfering signal from the known transmit signal and subtracting it from the received signal. Conventional cancellation approaches rely on a mathematical model of the self-interference generation. However, these cannot capture effects beyond their model assumptions. With neural networkbased cancellation, present interference-generating effects are captured during data-driven training. In this work, we combine such a cancellation method with a neural network-based receiver for the demodulation of the signal-of-interest. Simulation results with 5G NR-conform waveforms show that this approach is both well-performing and adaptive to many self-interference and radio channel scenarios. Our approach is able to cancel selfinterference that has undergone power amplifier nonlinearity, IQ imbalance, and a frequency-selective channel down to a signalto- interference ratio of 0.0 dB.
@inproceedings{10925912,
abstract = {In frequency division duplex systems with simultaneous transmission and reception, receivers suffer from outof- band emissions by the wireless transmitter leaking into the receive band. This so-called self-interference can be cancelled by means of estimating the interfering signal from the known transmit signal and subtracting it from the received signal. Conventional cancellation approaches rely on a mathematical model of the self-interference generation. However, these cannot capture effects beyond their model assumptions. With neural networkbased cancellation, present interference-generating effects are captured during data-driven training. In this work, we combine such a cancellation method with a neural network-based receiver for the demodulation of the signal-of-interest. Simulation results with 5G NR-conform waveforms show that this approach is both well-performing and adaptive to many self-interference and radio channel scenarios. Our approach is able to cancel selfinterference that has undergone power amplifier nonlinearity, IQ imbalance, and a frequency-selective channel down to a signalto- interference ratio of 0.0 dB.},
added-at = {2025-03-25T17:16:22.000+0100},
author = {Beckworth, Mike and Fuchs, Ephraim and Fischer, Moritz Benedikt and ten Brink, Stephan},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/22da54fbc86432e47c239c0c98a168620/moritzfischer},
booktitle = {European Wireless 2024; 29th European Wireless Conference},
description = {Neural Network-Based Self-Interference Cancellation for Frequency Division Duplex | VDE Conference Publication | IEEE Xplore},
interhash = {9834f812e5f2a59ca06d91b59e3f1fe8},
intrahash = {2da54fbc86432e47c239c0c98a168620},
keywords = {myown},
month = {Sep.},
pages = {47-52},
timestamp = {2025-03-25T17:16:22.000+0100},
title = {Neural Network-Based Self-Interference Cancellation for Frequency Division Duplex},
year = 2024
}
