2025/07/09 by Yizhuo Wu, Ang Li, Wu, Yizhuo +3
Engineering · #Advanced Power Amplifier Design #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Electrical engineering #PAPR reduction in OFDM #Radio Frequency Integrated Circuit Design #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2507.06849
openalex publication_date 2025/07/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.