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Linearity of sequential molecular signals in turbulent diffusion channels

Linearity of sequential molecular signals in turbulent diffusion channels
Linearity of sequential molecular signals in turbulent diffusion channels
Molecular communication underpins biological system coordination across multiple spatial and temporal scales. Whilst significant research has focused on micro-scale diffusion dominated channels, far less is understood of macro-scale flow dominated channels. The latter introduces complex fluid dynamic forces, one of which is turbulent diffusion. Molecular Communication via Turbulent Diffusion (MCvTD) more accurately reflects realistic molecular channels in both pheromone signaling and chemical engineering. Current literature assumes linear combining between sequential molecular signals, but this assumption may not hold when turbulence is introduced. Here, we use computational fluid dynamics (CFD) simulation to show that sequential MCvTD signals do indeed linearly combine. This is a non-trivial and non-intuitive result and our conclusion allows the research field to leverage on existing linear combining signal analysis. To ensure robustness of our results, we test for the received signal strength and Inter-Symbol-Interference (ISI) under different concentrations, co-flow rate, and the information sequence. Also, we introduce a basis for the channel model in a way that for any k sequential signals in which k ≥ 4, by understanding the 1 ≤ k ≤ 3 signals and the last signal, we can represent the other signals. We expect these results to be useful to both molecular communication and biological signaling researchers.
molecular communication, turbulence, CFD
1550-3607
IEEE
Abbaszadeh, Mahmoud
594e03c0-a134-4b95-b1db-35171b8f0561
Yilmaz, H. Birkan
b7a0c004-a9f8-43c3-9a2a-72f518796c7b
Thomas, Peter J.
e147321c-1bec-4acb-96a5-5871d01f773f
Guo, Weisi
8e0fb220-0bc2-4d70-8e68-ee7fbd6e7b01
Abbaszadeh, Mahmoud
594e03c0-a134-4b95-b1db-35171b8f0561
Yilmaz, H. Birkan
b7a0c004-a9f8-43c3-9a2a-72f518796c7b
Thomas, Peter J.
e147321c-1bec-4acb-96a5-5871d01f773f
Guo, Weisi
8e0fb220-0bc2-4d70-8e68-ee7fbd6e7b01

Abbaszadeh, Mahmoud, Yilmaz, H. Birkan, Thomas, Peter J. and Guo, Weisi (2019) Linearity of sequential molecular signals in turbulent diffusion channels. In ICC 2019 - 2019 IEEE International Conference on Communications (ICC). IEEE..

Record type: Conference or Workshop Item (Paper)

Abstract

Molecular communication underpins biological system coordination across multiple spatial and temporal scales. Whilst significant research has focused on micro-scale diffusion dominated channels, far less is understood of macro-scale flow dominated channels. The latter introduces complex fluid dynamic forces, one of which is turbulent diffusion. Molecular Communication via Turbulent Diffusion (MCvTD) more accurately reflects realistic molecular channels in both pheromone signaling and chemical engineering. Current literature assumes linear combining between sequential molecular signals, but this assumption may not hold when turbulence is introduced. Here, we use computational fluid dynamics (CFD) simulation to show that sequential MCvTD signals do indeed linearly combine. This is a non-trivial and non-intuitive result and our conclusion allows the research field to leverage on existing linear combining signal analysis. To ensure robustness of our results, we test for the received signal strength and Inter-Symbol-Interference (ISI) under different concentrations, co-flow rate, and the information sequence. Also, we introduce a basis for the channel model in a way that for any k sequential signals in which k ≥ 4, by understanding the 1 ≤ k ≤ 3 signals and the last signal, we can represent the other signals. We expect these results to be useful to both molecular communication and biological signaling researchers.

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More information

Published date: July 2019
Keywords: molecular communication, turbulence, CFD

Identifiers

Local EPrints ID: 445498
URI: http://eprints.soton.ac.uk/id/eprint/445498
ISSN: 1550-3607
PURE UUID: ca0bf301-e856-49bd-b274-fd81fea53f13
ORCID for Mahmoud Abbaszadeh: ORCID iD orcid.org/0000-0002-3736-2086

Catalogue record

Date deposited: 11 Dec 2020 17:33
Last modified: 28 Apr 2022 02:31

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Contributors

Author: H. Birkan Yilmaz
Author: Peter J. Thomas
Author: Weisi Guo

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