Identification of non-linear RF systems using backpropagation

Andreas Toftegaard Kristensen, Andreas Burg, Alexios Balatsoukas-Stimming

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

9 Citaten (Scopus)

Samenvatting

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.

Originele taal-2Engels
Titel2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings
UitgeverijInstitute of Electrical and Electronics Engineers
Aantal pagina's6
ISBN van elektronische versie978-1-7281-7440-2
DOI's
StatusGepubliceerd - jun. 2020
Evenement2020 IEEE International Conference on Communications (ICC 2020) - Dublin, Ierland
Duur: 7 jun. 202011 jun. 2020

Congres

Congres2020 IEEE International Conference on Communications (ICC 2020)
Land/RegioIerland
StadDublin
Periode7/06/2011/06/20

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