TCN-DPD: Parameter-Efficient Temporal Convolutional Networks for Wideband Digital Predistortion

Huanqiang Duan, Manno Versluis, Qinyu Chen, Leo C. N. de Vreede, Chang Gao
6/13/2025
eess.SPcs.AI

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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_200MHz dataset, TCN-DPD achieves simulated ACPRs of -51.58/-49.26 dBc (L/R), EVM of -47.52 dB, and NMSE of -44.61 dB with 500 parameters and maintains superior linearization than prior models down to 200 parameters, making it promising for efficient wideband PA linearization.

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Cite this paper

@article{duan2025tcndpd,
  title  = {TCN-DPD: Parameter-Efficient Temporal Convolutional Networks for Wideband Digital Predistortion},
  author = {Huanqiang Duan and Manno Versluis and Qinyu Chen and Leo C. N. de Vreede and Chang Gao},
  year   = {2025},
  eprint = {2506.12165},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2506.12165v1}
}

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