Digital Pre Distortion

Digital pre-distortion (DPD) techniques improve the linearity of power amplifiers (PAs) in radio frequency systems, enhancing signal quality and reducing distortion. Current research emphasizes developing energy-efficient DPD algorithms, particularly focusing on deep neural networks (DNNs), including convolutional neural networks (CNNs) and recurrent neural networks (RNNs) like gated recurrent units (GRUs), to achieve real-time performance and reduce computational complexity. This focus is driven by the need to handle increasingly wider bandwidths and higher data rates in modern communication systems, with open-source frameworks facilitating research and benchmarking efforts. The development of efficient and adaptable DPD methods is crucial for improving the performance and energy efficiency of wireless communication technologies.