Before AI Takes Over: Rethinking Nonlinear Signal Processing in Communications

Abstract

There is an urgent reflection on traditional nonlinear signal processing methods in communications before Artificial Intelligence (AI) dominates the field. It implies a need to reassess or reinterpret established theories and tools, highlighting the tension between data-driven and model-based approaches. This paper calls for preserving valuable insights from classical signal processing while exploring how they can coexist or integrate with emerging AI methods.