# Artificial Intelligence for Dynamical Systems in Wireless Communications: Modeling for the Future

## TL;DR

This paper reviews the application of dynamical systems theory to wireless communications over the past 30 years, emphasizing its potential for future research and the need to revisit this approach with current resources and demands.

## Contribution

It provides a comprehensive overview of dynamical systems applications in wireless communications and advocates for renewed interest and research in this area.

## Key findings

- Dynamical systems theory has been applied across all media layers in wireless communications.
- Reservoir computing originated in wireless communications and should be further integrated.
- Current resources and demands make the application of dynamical systems feasible and promising.

## Abstract

Dynamical systems are no strangers in wireless communications. Our story will necessarily involve chaos, but not in the terms secure chaotic communications have introduced it: we will look for the chaos, complexity and dynamics that already exist in everyday wireless communications. We present a short overview of dynamical systems and chaos before focusing on the applications of dynamical systems theory to wireless communications in the past 30 years, ranging from the modeling on the physical layer to different kinds of self-similar traffic encountered all the way up to the network layer. The examples of past research and its implications are grouped and mapped onto the media layers of ISO OSI model to show just how ubiquitous dynamical systems theory can be and to trace the paths that may be taken now. When considering the future paths, we argue that the time has come for us to revive the interest in dynamical systems for wireless communications. It did not happen already because of the big question: can we afford observing systems of our interest as dynamical systems and what are the trade-offs? The answers to these questions are dynamical systems of its own: they change not only with the modeling context, but also with time. In the current moment the available resources allow such approach and the current demands ask for it. Reservoir computing, the major player in dynamical systems-related learning originated in wireless communications, and to wireless communications it should return.