Kristensen, Andreas ToftegaardBurg, AndreasBalatsoukas-Stimming, Alexios2021-03-262021-03-262021-03-262020-01-0110.1109/ICCWorkshops49005.2020.9145367https://infoscience.epfl.ch/handle/20.500.14299/176390WOS:000607199300234In this work, we use deep unfolding to view cascaded non-linear RF systems as model-based neural networks. This view enables the direct use of a wide range of neural network tools and optimizers to efficiently identify such cascaded models. We demonstrate the effectiveness of this approach through the example of digital self-interference cancellation in full-duplex communications where an IQ imbalance model and a non-linear PA model are cascaded in series. For a self-interference cancellation performance of approximately 44.5 dB, the number of model parameters can be reduced by 74% and the number of operations per sample can be reduced by 79% compared to an expanded linear-in-parameters polynomial model.Engineering, Electrical & ElectronicTelecommunicationsEngineeringbackpropagationmemory polynomialparallel hammerstein modelvolterra seriessi cancellationdigital predistortionmodeltext::conference output::conference proceedings::conference paper