L. Rusch

Digital Predistortion for Non-Differentiable Nonlinear Systems: A Reinforcement Learning Framework With Transfer Learning

The analysis demonstrates that the RL-based approach, enabled by an effective neural network initialization strategy, surpasses traditional methods and ML-based DPD schemes such as DLA and ILA and provides a scalable and efficient solution for compensating pattern-dependent nonlinearities in high-speed optical communications.