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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2507.06849 |
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| _version_ | 1866917149877993472 |
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| author | Wu, Yizhuo Li, Ang Gao, Chang |
| author_facet | Wu, Yizhuo Li, Ang Gao, Chang |
| contents | Neural network (NN)-based Digital Predistortion (DPD) has demonstrated superior performance in improving signal quality in wideband radio frequency (RF) power amplifiers (PAs) employing complex modulation. However, NN DPDs usually rely on a large number of parameters for effective linearization and can significantly contribute to the energy consumption of the digital back-end in RF systems. This paper presents OpenDPDv2, an open-source, end-to-end framework that unifies PA modeling, NN-DPD learning, and deployment-oriented model optimization to reduce inference energy while preserving linearization performance. OpenDPDv2 introduces TRes-DeltaGRU, a delta-RNN DPD architecture with a lightweight temporal residual path that improves robustness under aggressive temporal sparsity, and it supports joint optimization of temporal sparsity and fixed-point quantization. On a 3.5 GHz GaN Doherty PA driven by a TM3.1a 200 MHz 256-QAM OFDM signal, the FP32 TRes-DeltaGRU model achieves ACPR of -59.9 dBc and EVM of -42.1 dB. By combining quantization with dynamic temporal sparsity, the model reduces inference energy by 4.5x while maintaining -51.8 dBc ACPR and -35.2 dB EVM at 56% temporal sparsity. Code, datasets, and documentation are publicly available at https://github.com/lab-emi/OpenDPD. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06849 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OpenDPDv2: A Unified Learning and Optimization Framework for Neural Network Digital Predistortion Wu, Yizhuo Li, Ang Gao, Chang Signal Processing Artificial Intelligence Neural network (NN)-based Digital Predistortion (DPD) has demonstrated superior performance in improving signal quality in wideband radio frequency (RF) power amplifiers (PAs) employing complex modulation. However, NN DPDs usually rely on a large number of parameters for effective linearization and can significantly contribute to the energy consumption of the digital back-end in RF systems. This paper presents OpenDPDv2, an open-source, end-to-end framework that unifies PA modeling, NN-DPD learning, and deployment-oriented model optimization to reduce inference energy while preserving linearization performance. OpenDPDv2 introduces TRes-DeltaGRU, a delta-RNN DPD architecture with a lightweight temporal residual path that improves robustness under aggressive temporal sparsity, and it supports joint optimization of temporal sparsity and fixed-point quantization. On a 3.5 GHz GaN Doherty PA driven by a TM3.1a 200 MHz 256-QAM OFDM signal, the FP32 TRes-DeltaGRU model achieves ACPR of -59.9 dBc and EVM of -42.1 dB. By combining quantization with dynamic temporal sparsity, the model reduces inference energy by 4.5x while maintaining -51.8 dBc ACPR and -35.2 dB EVM at 56% temporal sparsity. Code, datasets, and documentation are publicly available at https://github.com/lab-emi/OpenDPD. |
| title | OpenDPDv2: A Unified Learning and Optimization Framework for Neural Network Digital Predistortion |
| topic | Signal Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2507.06849 |