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Information Transmission in Dynamical Networks: The Normal Network Case
Giacomo Baggio1, Virginia Rutten2, Guillaume Hennequin3
1Department of Mechanical Engineering, University of California at Riverside, Riverside, CA, USA.
Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control
|July 23, 2019
Summary
This study introduces a new model for information transmission in linear networks, considering inter-symbol interference. Network structure, particularly for normal adjacency matrices, impacts the maximum information rate, with non-normal architectures showing potential benefits.
Area of Science:
- Network science
- Information theory
- Dynamical systems
Background:
- Networked systems in physics and biology rely on reliable information processing.
- Understanding information transmission dynamics in these systems is crucial.
Purpose of the Study:
- To propose a novel framework for modeling information transmission in linear dynamical networks.
- To quantify information transmission using Shannon information rate.
- To investigate the influence of network connectivity on information transmission.
Main Methods:
- Modeling information propagation using a digital communication protocol incorporating inter-symbol interference.
- Applying Shannon information rate to measure reliable information transfer over a fixed time window.
- Analyzing the impact of network adjacency matrix properties, specifically normal matrices, on information rate.
Main Results:
- The maximum achievable information rate in networks with a normal adjacency matrix is determined solely by the spectrum of the adjacency matrix.
- Numerical results indicate that non-normal network architectures may enhance information transmission within the proposed framework.
Conclusions:
- Network architecture significantly influences information transmission capacity.
- Non-normal network structures present a promising avenue for improving information transmission efficiency in dynamical systems.
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