Dynamic Analysis of Digital Chaotic Maps via State-Mapping Networks

TL;DR

This paper investigates the structure of digital chaotic maps using state-mapping networks, revealing properties like scale-freeness, to improve understanding and performance of chaos-based pseudo-random generators in finite-precision digital systems.

Contribution

It introduces a novel network-based approach to analyze digital chaotic maps, providing new insights into their dynamical properties in finite-precision arithmetic.

Findings

Proves the scale-free property of the Logistic map's SMN.

Extends analysis to floating-point arithmetic and other chaotic maps.

Facilitates better classification and enhancement of pseudo-random sequences.

Abstract

Chaotic dynamics is widely used to design pseudo-random number generators and for other applications such as secure communications and encryption. This paper aims to study the dynamics of discrete-time chaotic maps in the digital (i.e., finite-precision) domain. Differing from the traditional approaches treating a digital chaotic map as a black box with different explanations according to the test results of the output, the dynamical properties of such chaotic maps are first explored with a fixed-point arithmetic, using the Logistic map and the Tent map as two representative examples, from a new perspective with the corresponding state-mapping networks (SMNs). In an SMN, every possible value in the digital domain is considered as a node and the mapping relationship between any pair of nodes is a directed edge. The scale-free properties of the Logistic map's SMN are proved. The analytic results are further extended to the scenario of floating-point arithmetic and for other chaotic maps. Understanding the network structure of a chaotic map's SMN in digital computers can facilitate counteracting the undesirable degeneration of chaotic dynamics in finite-precision domains, helping also classify and improve the randomness of pseudo-random number sequences generated by iterating chaotic maps.