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Physical Channel Model for Molecular Communications

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Modeling, Methodologies and Tools for Molecular and Nano-scale Communications

Part of the book series: Modeling and Optimization in Science and Technologies ((MOST,volume 9))

Abstract

This chapter comes up with a brief overview of molecular communication models and modulation techniques by reviewing current research works found in the literature. The chapter also provides with an analysis of molecular communication in free diffusion-based molecular communication channel. In this model, the trasmitter nanomachine releases messenger molecules, the molecules diffuse through the channel, and the receiver nanomachine counts the received molecules to decode the information. We consider free diffusion of molecules where no additional force is required. Such a channel is referred to as the diffusion channel and can be modeled by using Ficks law of diffusion. Diffusion coefficient describes the tendency of propagation of the messenger molecules through the medium. Analysis shows that, channel memory offers a significant impact on performance.

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References

  1. Freitas RA (1999) Nanomedicine, vol 1: Basic capabilities. Landes Bioscience Georgetown, TX (1999)

    Google Scholar 

  2. Nakano T, Moore MJ, Wei F, Vasilakos AV, Shuai J (2012) Molecular communication and networking: opportunities and challenges. IEEE Trans Nanobiosci 11:135–148

    Article  Google Scholar 

  3. Akyildiz IF, Brunetti F, Blázquez C (2008) Nanonetworks: a new communication paradigm. Comput. Netw. 52:2260–2279

    Article  Google Scholar 

  4. Sawai H (2011) Biological functions for information and communication technologies: theory and inspiration. Springer

    Google Scholar 

  5. Moore MJ, Suda T, Oiwa K (2009) Molecular communication: modeling noise effects on information rate. IEEE Trans Nanobiosci 8:169–180

    Article  Google Scholar 

  6. Kuran M, Yilmaz HB, Tugcu T, Akyildiz IF (2011) Modulation techniques for communication via diffusion in nanonetworks. In: 2011 IEEE International Conference on Communications (ICC), pp 1–5

    Google Scholar 

  7. Kim NR, Chae CB (2013) Novel modulation techniques using isomers as messenger molecules for nano communication networks via diffusion. IEEE J Sel Areas Commun 31:847–856

    Article  Google Scholar 

  8. Kadloor S, Adve R (2009) A framework to study the molecular communication system. In: Proceedings of 18th International Conference on Computer Communication Networks, pp 1–6

    Google Scholar 

  9. ShahMohammadian H, Messier GG, Magierowski S (2012) Optimum receiver for molecule shift keying modulation in diffusion-based molecular communication channels. Nano Commun Netw 3:183–195

    Article  Google Scholar 

  10. Tyrrell H, Harris K (1984) Diffusion in liquids: a theoretical and experimental study. Butterworth-Heinemann

    Google Scholar 

  11. Kuran M, Yilmaz HB, Tugcu T, Zerman B (2010) Energy model for communication via diffusion in nanonetworks. Nano Commun Netw 1:86–95

    Google Scholar 

  12. Redner S (2001) A guide to first-passage processes. Cambridge University Press

    Google Scholar 

  13. Srinivas KV, Eckford AW, Adve RS (2012) Molecular communication in fluid media: the additive inverse Gaussian noise channel. IEEE Trans Inf Theory 58:4678–4692

    Article  MathSciNet  Google Scholar 

  14. Khormuji MN (2011) On the capacity of molecular communication over the AIGN channel. In: 2011 45th annual Conference on Information Sciences and Systems (CISS), pp 1–4

    Google Scholar 

  15. Miorandi D (2011) A stochastic model for molecular communications. Nano Commun Netw 2:205–212

    Article  Google Scholar 

  16. Einolghozati A, Sardari M, Beirami A, Fekri F (2011) Capacity of discrete molecular diffusion channels. In: 2011 IEEE international symposium on Information Theory Proceedings (ISIT), pp 723–727

    Google Scholar 

  17. Berg HC (1993) Random walks in biology. Princeton University Press

    Google Scholar 

  18. De Kievit TR, Iglewski BH (2000) Bacterial quorumsensing in pathogenic relationships. Infecti Immun 68:4839–4849

    Article  Google Scholar 

  19. Eckford AW (2007) Nanoscale communication with brownian motion. In: Proceedings of 41st annual conference on information sciences and systems, pp 160–165

    Google Scholar 

  20. Eckford AW (2007) Achievable information rates for molecular communication with distinct molecules. In: Proceedings of workshop computer communications from biological systems: theory and applications, pp 313–315

    Google Scholar 

  21. Okaie Y, Nakano T (2012) Nanomachine placement strategies for detecting Brownian molecules in nanonetworks. In: Proceedings of IEEE Wireless Communication Networking Conference (WCNC), pp 1755–1759

    Google Scholar 

  22. Eckford AW, Farsad N, Hiyama S, Moritani Y (2010) Microchannel molecular communication with nanoscale carriers: Brownian motion versus active transport. In: Proceedings of IEEE international conference on nanotechnologies, pp 854–858

    Google Scholar 

  23. Eckford AW (2009) Timing information rates for active transport molecular communication. In: Nano-Networks, pp 24–28

    Google Scholar 

  24. Alberts B, Bray D, Lewis J, Raff M, Roberts K, Watson JD (1994) Molecular biology of the cell (1994) Garland. New York, pp 139–194

    Google Scholar 

  25. Nakano T, Suda T, Koujin T, Haraguchi T, Hiraoka Y (2007) Molecular communication through gap junction channels: system design, experiments and modeling. In: Proceedings of International Conference on Bio-Inspired Models of Network, Information and Computing Systems, BIONETICS, pp 139–146 (2007)

    Google Scholar 

  26. Hiyama H, Moritani Y, Suda T (2009) A biochemically engineered molecular communication system. Nano Networks, pp 85–94

    Google Scholar 

  27. Oiwa K, Sakakibara H (2005) Recent progress in dynein structure and mechanism. Current Opin Cell Biol 17:98–103

    Article  Google Scholar 

  28. Shima T, Kon T, Imamula K, Ohkura R, Sutoh K (2006) Two modes of microtubule sliding driven by cytoplasmic dynein. Proc Natl Acad Sci 103:17736–17740

    Article  Google Scholar 

  29. Toba S, Oiwa K (2006) Swing or embrace. New aspects of motility inspired by dynein structure in situ. Bioforum Eur 10:14–16

    Google Scholar 

  30. Kuscu M, Akan OB (2012) A physical channel model and analysis for nanoscale molecular communication with Forster Resonance Energy Transfer (FRET). IEEE Trans Nanotechnol 11:200–207

    Article  Google Scholar 

  31. Kabir MH, Kwak KS (2013) Molecular nanonetwork channel model. Electron Lett 49:1285–1287

    Article  Google Scholar 

  32. Socolofsky SA, Jirka GH (2005) CVEN 489-501: Special Topics in Mixing and Transport Processes in the Environment. In: EngineeringLectures. 5th edn., vol 3136. Coastal and Ocean Engineering Division, Texas A&M University, MS, p 77843

    Google Scholar 

  33. Kabir MH, Kwak KS (2014) Effect of memory on BER in molecular communication. Electron Lett 50:71–72

    Article  Google Scholar 

  34. Leeson MS, Higgins MD (2012) Forward error correction for molecular communications. Nano Commun Netw 3:161–167

    Article  Google Scholar 

  35. Pierobon M, Akyildiz IF (2011) Information capacity of diffusion-based molecular communication in nanonetworks. In: INFOCOM, 2011 Proc IEEE, pp 506–510

    Google Scholar 

  36. Nakano T, Okaie Y, Jian-Qin L (2012) Channel model and capacity analysis of molecular communication with Brownian motion. IEEE Commun Lett 16:797–800

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MEST) (No. 2010-0018116).

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Authors and Affiliations

  1. Inha University, Incheon, 402-751, Korea

    Humaun Kabir

  2. UWB Wireless Communications Research Center, School of Information and Communication Engineering, Inha University, Incheon, 402-751, Korea

    Kyung Sup Kwak

Authors
  1. Humaun Kabir
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  2. Kyung Sup Kwak
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Corresponding author

Correspondence to Humaun Kabir .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Massachusetts, Boston, Massachusetts, USA

    Junichi Suzuki

  2. Graduate School of Biological Sciences, Osaka University, Osaka, Japan

    Tadashi Nakano

  3. 57006D Applied Science Building, Pennsylvania State University, State College, Pennsylvania, USA

    Michael John Moore

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Kabir, H., Kwak, K.S. (2017). Physical Channel Model for Molecular Communications. In: Suzuki, J., Nakano, T., Moore, M. (eds) Modeling, Methodologies and Tools for Molecular and Nano-scale Communications. Modeling and Optimization in Science and Technologies, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-319-50688-3_3

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  • DOI: https://doi.org/10.1007/978-3-319-50688-3_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50686-9

  • Online ISBN: 978-3-319-50688-3

  • eBook Packages: EngineeringEngineering (R0)

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