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Efficient communication over complex dynamical networks: The role of matrix non-normality
Giacomo Baggio1, Virginia Rutten2,3, Guillaume Hennequin4
1Department of Information Engineering, University of Padova, via Gradenigo, 6/B I-35131 Padova, Italy.
Science Advances
|June 11, 2020
Summary
Non-normal networks enhance information transmission by canceling noise, unlike normal networks that amplify it. This finding aids in designing better communication systems.
Area of Science:
- Network science
- Information theory
- Systems engineering
Background:
- Communication in natural and engineered systems relies on dynamic networks.
- Understanding network structure, noise, and interference is crucial for efficient information transmission.
Purpose of the Study:
- To develop a framework for analyzing how network structure, noise, and interference affect transmission performance in complex linear dynamic networks.
- To compare information transmission in normal versus non-normal networks.
Main Methods:
- Mathematical modeling of complex networks with linear dynamics.
- Analysis of information propagation and transformation under noisy conditions.
- Examination of the impact of network topology on signal-to-noise ratio.
Main Results:
- Normal networks, decomposable into low-dimensional channels, are highly susceptible to readout noise.
- Non-normal networks can mitigate noise by transiently amplifying specific input dimensions.
- Non-normal networks exhibit higher net information throughput compared to normal networks.
Conclusions:
- Network non-normality offers a mechanism for effective noise cancellation in communication systems.
- The study provides insights for designing novel communication networks and optimizing existing ones.
- Transient amplification in non-normal networks is key to improved transmission quality.
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