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Information Transmission in Dynamical Networks: The Normal Network Case
- Source :
- CDC
- Publication Year :
- 2018
- Publisher :
- IEEE, 2018.
-
Abstract
- Reliable information processing is a hallmark of many physical and biological networked systems. In this paper, we propose a novel framework for modelling information transmission within a linear dynamical network. Information propagation is modelled by means of a digital communication protocol that takes into account the realistic phenomenon of inter-symbol interference. Building on this framework, we adopt Shannon information rate to quantify the amount of information that can be reliably sent over the network within a fixed time window. We investigate how the latter information metric is affected by the connectivity structure of the network. Here, we focus in particular on networks characterized by a normal adjacency matrix. We show that for such networks the maximum achievable information rate depends only on the spectrum of the adjacency matrix. We then provide numerical results suggesting that non-normal network architectures could benefit information transmission in our framework.
- Subjects :
- 0301 basic medicine
Network architecture
Computer science
Distributed computing
Information processing
Code rate
Article
03 medical and health sciences
Channel capacity
030104 developmental biology
0302 clinical medicine
Metric (mathematics)
Entropy (information theory)
Adjacency matrix
Communications protocol
030217 neurology & neurosurgery
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2018 IEEE Conference on Decision and Control (CDC)
- Accession number :
- edsair.doi.dedup.....4ea1edc3b7de95ffb57022a896cb74bc