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In this work, we use deep unfolding to view cascaded non-linear RF systems asmodel-based neural networks. This view enables the direct use of a wide rangeof neural network tools and optimizers to efficiently identify such cascadedmodels. We demonstrate the effectiveness of this approach through the exampleof digital self-interference cancellation in full-duplex communications wherean IQ imbalance model and a non-linear PA model are cascaded in series. For aself-interference cancellation performance of approximately 44.5 dB, the numberof model parameters can be reduced by 74% and the number of operations persample can be reduced by 79% compared to an expanded linear-in-parameterspolynomial model.

Identification of Non-Linear RF Systems Using Backpropagation