Yue Sun
ABSTRACT
Molecular communication (MC) is a bio-inspired communication method in future Nano-networks. This paper follows a new bio-phenomenon into MC, namely, blood vessels. While previous work on blood vessels or blood capillary focus on free diffusion without drift and described by Ficks second law, a more precise, kinetic stochastic differential equation, Langevin equation is used instead to model the blood flow and drift. Further more, blood flow in blood vessels considered a laminar flow model rather than turbulent flow. The solution of Fokker-Planck equation, corresponding to Langevin equation, is provided by drift coefficient and diffusion coefficient in the blood vessels environment. Finally, we derive channel capacity expression for single access channel. Numerical results present the relationship between channel capacity and parameters in the blood vessels.
- Akyildiz, I. F., Brunetti, F., and Blázquez, C. Nanonetworks: A new communication paradigm. Computer Networks 52, 12 (2008), 2260--2279. Google Scholar
Digital Library
- Atakan, B., and Akan, O. B. Single and Multiple-Access Channel Capacity in Molecular Nanonetworks.Google Scholar
- Brown, R., and Einstein, A. Brownian Motion : Langevin Equation.Google Scholar
- Chahibi, Y., and Akyildiz, I. F. Molecular communication noise and capacity analysis for particulate drug delivery systems. IEEE Transactions on Communications 62, 11 (2014), 3891--3903. Google ScholarCross Ref
- Coffey, W. T., Kalmykov, Y. P., and Waldron, J. T. The Langevin Equation With Applications to Stochastic Problems in Physics, Chemistry and Electrical Engineering. Chemical Physics 14, SEPTEMBER (2004), 678.Google Scholar
- Fa, K. S. Solution of Fokker-Planck equation for a broad class of drift and diffusion coefficients. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics 84, 1 (2011). Google ScholarCross Ref
- Felicetti, L., Femminella, M., and Reali, G. Simulation of molecular signaling in blood vessels: Software design and application to atherogenesis. Nano Communication Networks 4, 3 (sep 2013), 98--119. Google ScholarCross Ref
- Felicetti, L., Femminella, M., Reali, G., Gresele, P., Malvestiti, M., and Daigle, J. N. Modeling CD40-based molecular communications in blood vessels. IEEE Transactions on Nanobioscience 13, 3 (2014), 230--243. Google ScholarCross Ref
- Feynman, R. P. There ' s Plenty of Room at the Bottom. Journal of Microeleciromechanical Systems 1, 1 (1992), 60--66. Google ScholarCross Ref
- Liu, Q., and Yang, K. Multiple-access channel capacity of diffusion and ligand-based molecular communication. Proceedings of the 16th ACM international conference on Modeling, analysis & simulation of wireless and mobile systems - MSWiM '13 (2013), 151--158. Google ScholarDigital Library
- Liu, Q., Yang, K., and Leng, S. Channel Capacity Analysis for Molecular Communication with Different Emission Schemes.Google Scholar
- Malaka, R., Ragg, T., and Hammer, M. Kinetic models of odor transduction implemented as artificial neural networks - Simulations of complex response properties of honeybee olfactory neurons. Biological Cybernetics 73, 3 (1995), 195--207. Google ScholarDigital Library
- Parcerisa Giné, L., and Akyildiz, I. F. Molecular communication options for long range nanonetworks. Computer Networks 53, 16 (nov 2009), 2753--2766. Google ScholarDigital Library
- Pierobon, M., and Akyildiz, I. F. Capacity of a diffusion-based molecular communication system with channel memory and molecular noise. IEEE Transactions on Information Theory 59, 2 (2013), 942--954. Google ScholarDigital Library
- Rubin, K. J., Pruessner, G., and Pavliotis, G. a. Mapping multiplicative to additive noise. Journal of Physics A: Mathematical and Theoretical 47, 19 (2014), 195001.Google ScholarCross Ref
- Serda, R. E., Godin, B., Blanco, E., Chiappini, C., and Ferrari, M. Multi-stage delivery nano-particle systems for therapeutic applications. Biochimica et Biophysica Acta - General Subjects 1810, 3 (2011), 317--329.Google Scholar
- Shannon, C. E. A mathematical theory of communication. The Bell System Technical Journal 27 (1948), 379--423. Google ScholarCross Ref
- Siggers, J. Physiological Fluid Mechanics. Bioengineering, September (2009), 1--130.Google Scholar
- Srinivas, K. V., Eckford, A. W., and Adve, R. S. Molecular communication in fluid media: The additive inverse gaussian noise channel. IEEE Transactions on Information Theory 58, 7 (2012), 4678--4692. Google ScholarDigital Library
- Tan, J., Antony, T., and Liu, Y. Influence of Red Blood Cells on Nanoparticle Targeted Delivery. 1934--1946.Google Scholar
- Wagner, A. H., Gebauer, M., Pollok-Kopp, B., and Hecker, M. Cytokine-inducible CD40 expression in human endothelial cells is mediated by interferon regulatory factor-1. Blood 99, 2 (2002), 520--525. Google ScholarCross Ref
- White, F. Fluid Mechanics. McGraw-Hill,New York (2010), 862.Google Scholar
- Xing, X. Dynamic statistical information theory. Science in China Series G 49, 1 (2006), 1--37. Google ScholarCross Ref
- Zwanzig, R. Nonequilibrium statistical mechanics, vol. 54. 2001.Google Scholar
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