MP-DPD accepted to IMS 2024

The premier International Microwave Symposium (IMS) has accepted our paper “MP-DPD: Low-Complexity Mixed-Precision Neural Networks for Energy-Efficient Digital Pre-distortion of Wideband Power Amplifiers**,”** mainly done by our PhD students Yizhuo Wu and Ang Li. The paper was ranked in the Top 50 and invited to the IEEE Microwave and Wireless Technology Letters. This paper proposes a novel DPD with Fixed-Point-Floating-Point Mixed-Precision parameters and intermediate states, which can reduce DPD inference power consumption by 2.6X on 160MHz 1024-QAM OFDM signals without losing linearization performance (-38dB EVM).

MP-DPD is trained using our recently released OpenDPD framework, available at https://github.com/lab-emi/OpenDPD.