Before AI Takes Over: Rethinking Nonlinear Signal Processing in Communications

TL;DR

This paper advocates for a balanced approach that preserves classical nonlinear signal processing insights while integrating them with emerging AI techniques in communication systems.

Contribution

It highlights the importance of rethinking traditional nonlinear signal processing methods in the context of AI-driven communication technologies.

Findings

Emphasizes the need to reassess classical methods

Proposes integrating classical and AI approaches

Calls for a balanced future in signal processing

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.