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TCN-DPD

Parameter-Efficient Temporal Convolutional Networks for Wideband Digital Predistortion

Conference Paper (2025)
Author(s)

Huanqiang Duan (Student TU Delft)

Manno Versluis (Student TU Delft)

Qinyu Chen (Universiteit Leiden)

Leo C. N. de de Vreede (TU Delft - Electronics)

C. Gao (TU Delft - Electronics)

Research Group
Electronics
DOI related publication
https://doi.org/10.1109/IMS40360.2025.11103923
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Publication Year
2025
Language
English
Research Group
Electronics
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository as part of the Taverne amendment. More information about this copyright law amendment can be found at https://www.openaccess.nl. Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. @en
Pages (from-to)
1103-1106
ISBN (electronic)
9798331514099
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Digital predistortion (DPD) is essential for mitigating nonlinearity in RF power amplifiers, particularly for wideband applications. This paper presents TCN-DPD, a parameter-efficient architecture based on temporal convolutional networks, integrating noncausal dilated convolutions with optimized activation functions. Evaluated on the OpenDPD framework with the DPA_200 MHz dataset, TCN-DPD achieves simulated ACPRs of -51.58 /-49.26dBc (L/R), EVM of -47.52 dB, and NMSE of -44.61 dB with 500 parameters and maintain superior linearization than prior models down to 200 parameters, making it promising for efficient wideband PA linearization.

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File under embargo until 09-02-2026