Molecular Communications and Nanonetworks

#41045, Winter 2022/2023



Contents

This course will cover communication techniques and technologies for designing nanoscale networks, and not only that, but we will also include AI-based solutions. At the nanoscale, instead of connecting nodes via electromagnetic waves, we will study means to use molecules as a communication medium. In the physical layer, we will introduce models for the communication channels through molecular means, as well as for emitter and receiver schemes. In the link layer, we will address mechanisms for information flow and error control. The course will conduct various hands-on activities in the Matlab simulator to model the physical and link layers. Furthermore, we will research new AI-models to deal with pragmatic systems, where we want to optimize communication performance but we unknown the system parameters.

Learning Outcome

After completing the course, participants will be able to characterize the molecular communication scenarios. You will be able to apply theoretical knowledge to develop molecular communication networks with the support of AI-based solutions.

  • Describe the constituting elements of nanonetworks in molecular communication (MC) channels.
  • Apply theoretical knowledge to develop nanonetworks functionalities in the physical and link layers using molecules as information carriers.
  • Examine deep neural network (NN) architectures as innovative solutions for nanonetworks in the MC domain.
  • Develop deep NN modules to optimize communication links within MC simulators.

Course details

This master course will be held in English and all the course material is available in English. The course will be organized in lessons and labs, whith the major component through hands-on activities in labs. The topics in the course will cover For more information, slides, and required submissions, please see our ISIS page.

The course is organized in 16 Sessions aiming to follow the topics

Instructors

Schedule

  • Lecture
    Wednesday, 14:00-15:30h (c.t.), HFT-TA 340
  • Labs
    Wednesday, 14:00-15:30h (c.t.), HFT-TA 341

Literature

  • Tadashi Nakano, Andrew W. Eckford and Tokuko Haraguchi, Molecular Communication, Cambridge University Press, 2013. [DOI, BibTeX, More details]
  • Howard C. Berg, Random Walks in Biology, Princeton University Press, 1993. [BibTeX, More details]
  • Vahid Jamali, Arman Ahmadzadeh, Wayan Wicke, Adam Noel and Robert Schober, "Channel Modeling for Diffusive Molecular Communication - A Tutorial Review," Proceedings of the IEEE, vol. 107 (7), pp. 1256–1301, July 2019. [DOI, BibTeX, More details]
  • Mehmet Sukru Kuran, H. Birkan Yilmaz, Ilker Demirkol, Nariman Farsad and Andrea Goldsmith, "A Survey on Modulation Techniques in Molecular Communication via Diffusion," IEEE Communications Surveys & Tutorials, vol. 23 (1), pp. 7–28, January 2021. [DOI, BibTeX, More details]
  • Jean Philibert, "One and a Half Century of Diffusion: Fick, Einstein, Before and Beyond," Diffusion Fundamentals, vol. 4, pp. 1–19, November 2006. [DOI, BibTeX, More details]
  • Luca Felicetti, Mauro Femminella, Gianluca Reali, Tadashi Nakano and Athanasios V Vasilakos, "TCP-Like Molecular Communications," IEEE Journal on Selected Areas in Communications, vol. 32 (12), pp. 2354–2367, December 2014. [DOI, BibTeX, More details]
  • Baris Atakan and Ozgur B. Akan, "On Channel Capacity and Error Compensation in Molecular Communication," in Springer Transactions on Computational Systems Biology X, Biological and Biologically-inspired Communication, vol. LNBI 5410, Corrado Priami, Falko Dressler, Ozgur B. Akan and Alioune Ngom (Eds.), Springer, 2008, pp. 59–80. [DOI, BibTeX, More details]
  • Tadashi Nakano, "Molecular Communication: A 10 Year Retrospective," IEEE Transactions on Molecular, Biological and Multi-Scale Communications, vol. 3 (2), pp. 71–78, June 2017. [DOI, BibTeX, More details]
  • Tadashi Nakano, Tatsuya Suda, Y. Okaie, M. J. Moore and A. V. Vasilakos, "Molecular Communication Among Biological Nanomachines: A Layered Architecture and Research Issues," IEEE Transactions on NanoBioscience, vol. 13 (3), pp. 169–197, September 2014. [DOI, BibTeX, More details]
  • Steven Kay, Fundamentals of Statistical Signal Processing: Estimation Theory, Prentice Hall, 1998. [BibTeX, More details]
Last modified: 2026-04-24