  In 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.
