We plan to integrate and combine deep learning methods into/with conventional physical models in order to improve nonlinear modeling for electronic devices like e.g. communications and radio frequency (RF) transceivers. In particular, we will enhance or replace conventional signal processing models like e.g. Volterra series, Wiener-models, Hammerstein models or memory polynomials with new techniques of machine learning. Involved research fields are deep learning, deep unfolding, model –based neural networks, few shot learning, meta learning (transfer models from one device to another), sequence analysis using LSTMs and transformers/BERT.
Reinforcement learning methods should be considered for improving models that have delayed signals, therefore also delayed error or reward signals. The goal is to enhance existing physical models by new deep learning techniques to make model simulation faster without losing precision and modeling capacity or to improve existing models using new machine learning approaches. Also online and tracking models should be investigated, that is, models that adapt to the current environment like temperature, humidity, power consumption, voltage deviations, fast changing system parameters etc. These online methods might be combined or substituted by few shot and meta-learning methods.
Furthermore, we also plan to enhance and/or substitute model-based signal processing blocks like e.g. pre-coding, digital pre-distortion, nonlinear distortion mitigation, or adaptive channel estimation/equalization, by machine learning approaches.