An Information Theoretic Framework to Analyze Molecular Communication Systems Based on Statistical Mechanics
Over the past 10 years, molecular communication (MC) has established itself as a key transformative paradigm in communication theory. Inspired by chemical communications in biological systems, the focus of this discipline is on the modeling, characterization, and engineering of information transmiss...
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| Published in | Proceedings of the IEEE Vol. 107; no. 7; pp. 1230 - 1255 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.07.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0018-9219 1558-2256 |
| DOI | 10.1109/JPROC.2019.2927926 |
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| Abstract | Over the past 10 years, molecular communication (MC) has established itself as a key transformative paradigm in communication theory. Inspired by chemical communications in biological systems, the focus of this discipline is on the modeling, characterization, and engineering of information transmission through molecule exchange, with immediate applications in biotechnology, medicine, ecology, and defense, among others. Despite a plethora of diverse contributions, which has been published on the subject by the research community, a general framework to study the performance of MC systems is currently missing. This paper aims at filling this gap by providing an analysis of the physical processes underlying MC, along with their information-theoretic underpinnings. In particular, a mathematical framework is proposed to define the main functional blocks in MC, supported by general models from chemical kinetics and statistical mechanics. In this framework, the Langevin equation is utilized as a unifying modeling tool for molecule propagation in MC systems, and as the core of a methodology to determine the information capacity. Diverse MC systems are classified on the basis of the processes underlying molecule propagation, and their contribution in the Langevin equation. The classifications and the systems under each category are as follows: random walk (calcium signaling, neuron communication, and bacterial quorum sensing), drifted random walk (cardiovascular system, microfluidic systems, and pheromone communication), and active transport (molecular motors and bacterial chemotaxis). For each of these categories, a general information capacity expression is derived under simplifying assumptions and subsequently discussed in light of the specific functional blocks of more complex MC systems. Finally, in light of the proposed framework, a roadmap is envisioned for the future of MC as a discipline. |
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| AbstractList | Over the past 10 years, molecular communication (MC) has established itself as a key transformative paradigm in communication theory. Inspired by chemical communications in biological systems, the focus of this discipline is on the modeling, characterization, and engineering of information transmission through molecule exchange, with immediate applications in biotechnology, medicine, ecology, and defense, among others. Despite a plethora of diverse contributions, which has been published on the subject by the research community, a general framework to study the performance of MC systems is currently missing. This paper aims at filling this gap by providing an analysis of the physical processes underlying MC, along with their information-theoretic underpinnings. In particular, a mathematical framework is proposed to define the main functional blocks in MC, supported by general models from chemical kinetics and statistical mechanics. In this framework, the Langevin equation is utilized as a unifying modeling tool for molecule propagation in MC systems, and as the core of a methodology to determine the information capacity. Diverse MC systems are classified on the basis of the processes underlying molecule propagation, and their contribution in the Langevin equation. The classifications and the systems under each category are as follows: random walk (calcium signaling, neuron communication, and bacterial quorum sensing), drifted random walk (cardiovascular system, microfluidic systems, and pheromone communication), and active transport (molecular motors and bacterial chemotaxis). For each of these categories, a general information capacity expression is derived under simplifying assumptions and subsequently discussed in light of the specific functional blocks of more complex MC systems. Finally, in light of the proposed framework, a roadmap is envisioned for the future of MC as a discipline. |
| Author | Balasubramaniam, Sasitharan Akyildiz, Ian F. Pierobon, Massimiliano |
| Author_xml | – sequence: 1 givenname: Ian F. orcidid: 0000-0002-8099-3529 surname: Akyildiz fullname: Akyildiz, Ian F. email: ian@ece.gatech.edu organization: Broadband Wireless Networking Laboratory, School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA – sequence: 2 givenname: Massimiliano orcidid: 0000-0003-1074-6925 surname: Pierobon fullname: Pierobon, Massimiliano email: pierobon@cse.unl.edu organization: Department of Computer Science & Engineering, Molecular and Biochemical Telecommunications Laboratory, University of Nebraska-Lincoln, Lincoln, NE, USA – sequence: 3 givenname: Sasitharan surname: Balasubramaniam fullname: Balasubramaniam, Sasitharan email: sasib@tssg.org organization: Telecommunication Software and Systems Group, Waterford Institute of Technology, Waterford, Ireland |
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| SubjectTerms | Analytical models Biological system modeling Cardiovascular system Chemicals Communication Communication theory Communications systems Fokker–Planck equation Force information capacity Information theory Langevin equation Mathematical model Microfluidics Microorganisms molecular communication (MC) Molecular communication (telecommunication) Molecular motors nanonetworks Organic chemistry Poisson noise Propagation Random walk Reaction kinetics Statistical mechanics |
| Title | An Information Theoretic Framework to Analyze Molecular Communication Systems Based on Statistical Mechanics |
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