A novel Cardiff model coefficients extraction process based on artificial neural network

Tian, Mengyue, Bell, James ORCID: https://orcid.org/0000-0002-4815-2199, Azad, Ehsan, Quaglia, Roberto ORCID: https://orcid.org/0000-0003-3228-301X and Tasker, Paul ORCID: https://orcid.org/0000-0002-6760-7830 2023. A novel Cardiff model coefficients extraction process based on artificial neural network. Presented at: 2023 IEEE Topical Conference on RF/Microwave Power Amplifiers for Radio and Wireless ApplicationsED, MAY BE LINKED, Las Vegas, NV, USA, 22-25 January 2023. 2023 IEEE Topical Conference on RF/Microwave Power Amplifiers for Radio and Wireless Applications. IEEE, 10.1109/PAWR56957.2023.10046221

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Abstract

This paper presents a novel way to extract the Cardiff model coefficients. Analysis shows that the Cardiff model is easier for implementation in commercial RF design related software, while the Artificial Neural Network (ANN) based behavioral model provides more flexibility for extracting the coefficients of a model, the two methods have been combined. The load-pull setup, implemented in Keysight Advanced Design System (ADS), is used to acquire the pseudo-wave data of the Cree 10W transistor. By using the feedforward ANN structure together with a novel Cardiff model related backpropagation algorithm, a set of accurate coefficients can be extracted, whose Normalized Mean Squared Error (NMSE) is -54.73 dB as shown in this paper.

Item Type:	Conference or Workshop Item (UNSPECIFIED)
Date Type:	Published Online
Status:	In Press
Schools:	Engineering
Publisher:	IEEE
ISBN:	978-1-6654-9317-8
ISSN:	2473-4640
Last Modified:	21 Mar 2023 14:15
URI:	https://orca.cardiff.ac.uk/id/eprint/157845

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