AI Engineering of Nanonetworks [MolCom]

General Course Information



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.

Courses Given

Last modified: 2026-02-11