OpenDPDv2: A Unified Learning and Optimization Framework for Neural Network Digital Predistortion

• URL: http://arxiv.org/abs/2507.06849v1
• Date: Wed, 09 Jul 2025 13:54:47 GMT
• Title: OpenDPDv2: A Unified Learning and Optimization Framework for Neural Network Digital Predistortion
• Authors: Yizhuo Wu, Ang Li, Chang Gao,
• Abstract summary: This paper presents OpenDPDv2, a unified framework for PA modeling, DPD learning, and model optimization.<n> optimization techniques feature a novel DPD algorithm, TRes-DeltaGRU, alongside two energy-efficient methods.
• Score: 10.484441707788127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural network (NN)-based Digital Predistortion (DPD) stands out 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, a unified framework for PA modeling, DPD learning, and model optimization to reduce power consumption while maintaining high linearization performance. The optimization techniques feature a novel DPD algorithm, TRes-DeltaGRU, alongside two energy-efficient methods. The top-performing 32-bit floating-point (FP32) TRes-DeltaGRU-DPD model achieves an Adjacent Channel Power Ratio (ACPR) of -59.4 dBc and Error Vector Magnitude (EVM) of -42.1 dBc. By exploiting fixed-point quantization and dynamic temporal sparsity of input signals and hidden neurons, the inference energy of our model can be reduced by 4.5X while still maintaining -50.3 dBc ACPR and -35.2 dB EVM with 56% temporal sparsity. This was evaluated using a TM3.1a 200 MHz bandwidth 256-QAM OFDM signal applied to a 3.5 GHz GaN Doherty RF PA. OpenDPDv2 code, datasets, and documentation are publicly accessible at: https://github.com/lab-emi/OpenDPD.

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