Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.12165 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916915690078208 |
|---|---|
| author | Duan, Huanqiang Versluis, Manno Chen, Qinyu de Vreede, Leo C. N. Gao, Chang |
| author_facet | Duan, Huanqiang Versluis, Manno Chen, Qinyu de Vreede, Leo C. N. Gao, Chang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_12165 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TCN-DPD: Parameter-Efficient Temporal Convolutional Networks for Wideband Digital Predistortion Duan, Huanqiang Versluis, Manno Chen, Qinyu de Vreede, Leo C. N. Gao, Chang Signal Processing Artificial Intelligence 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. |
| title | TCN-DPD: Parameter-Efficient Temporal Convolutional Networks for Wideband Digital Predistortion |
| topic | Signal Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2506.12165 |