OpenDPD accepted to ISCAS 2024

We are thrilled to release OpenDPD: An Open-Source PyTorch-based End-to-End Learning & Benchmarking Framework for Wideband Power Amplifier Modeling and Digital Pre-Distortion (DPD). Authored by Yizhuo Wu, Gagan Deep Singh, Mohammad Reza Beikmirza, Leo de Vreede, Morteza Alavi, and Chang Gao (https://arxiv.org/abs/2401.08318). DPD enhances signal quality in wideband RF power amplifiers (PAs) and is a critical module in future 6G or Wi-Fi 7 wireless communication systems. OpenDPD comes with a free digital power amplifier I/Q dataset for you to train and benchmark machine learning (ML)/artificial intelligence (AI)-based DPDs and fairly compare them with other works. We will collaborate closely with our industrial partners to update this infrastructure periodically. OpenDPDv2 will come later this year with also free analog PA datasets.

OpenDPD code, datasets, and documentation are publicly available at https://github.com/lab-emi/OpenDPD. This work will be presented as a lecture at the 2024 IEEE International Symposium on Circuits and Systems (ISCAS), Singapore, in the Special Session: RFIC & AI: Pioneering New Wireless Communications.