Mixed-Precision Neural Networks for Energy-Efficient Digital Predistortion of Wideband Power Amplifiers

This work introduces mixed-precision neural networks that employ quantized low-precision fixed-point parameters to reduce the computational complexity and memory footprint of digital predistortion (DPD) for wideband power amplifiers, thereby lowering power consumption without compromising linearization efficacy.