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

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arXiv:2507.06849v3 Announce Type: replace-cross Abstract: Neural network (NN)-based Digital Predistortion (DPD) improves linearization for wideband radio frequency (RF) power amplifiers (PAs) but often increases the complexity of the digital back-end. This paper presents OpenDPDv2, an open-source end-to-end framework that unifies PA modeling, NN-DPD learning, and deployment-oriented optimization. OpenDPDv2 introduces temporal residual (TRes)-Delta Gated Recurrent Unit (DeltaGRU), a delta-RNN DPD architecture with a lightweight temporal residual path for robust operation under aggressive temporal sparsity, and supports joint optimization with 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 -59.9 dBc Adjacent Channel Power Ratio (ACPR) and -42.1 dB Error Vector Magnitude (EVM). With 56% temporal sparsity and W12A12 quantization, the model uses 450 active parameters while maintaining -51.8 dBc ACPR and -35.2 dB EVM. Timing-accurate Gem5-based evaluation further indicates a 4.5x reduction in forward-pass energy for this deployment-oriented operating point. Code, datasets, and documentation are publicly available at https://github.com/lab-emi/OpenDPD.

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