Identification of non-linear RF systems using backpropagation

Andreas Toftegaard Kristensen, Andreas Burg, Alexios Balatsoukas-Stimming

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

8 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)978-1-7281-7440-2
Publication statusPublished - Jun 2020
Event2020 IEEE International Conference on Communications (ICC 2020) - Dublin, Ireland
Duration: 7 Jun 202011 Jun 2020


Conference2020 IEEE International Conference on Communications (ICC 2020)


  • Backpropagation
  • Memory polynomial
  • Parallel Hammerstein model
  • SI cancellation
  • Volterra series


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