TCN-DPD: Parameter-Efficient Temporal Convolutional Networks for Wideband Digital Predistortion
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 19 references that are in the map. First posted on arXiv 13 Jun 2025.
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References · 8
- 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
- 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
- 2016WaveNet: A Generative Model for Raw Audiocited by 1,722
- 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,476
- 2015Multi-Scale Context Aggregation by Dilated Convolutionscited by 1,133
- 2019Probabilistic Forecasting with Temporal Convolutional Neural Networkcited by 63
- 2024OpenDPD: An Open-Source End-to-End Learning & Benchmarking Framework for Wideband Power Amplifier Modeling and Digital Pre-Distortioncited by 7
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Citing papers per year: 2025: 1.
1 paper.