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Transfer Function Models for Molecular Communication Channels

Subject Area Electronic Semiconductors, Components and Circuits, Integrated Systems, Sensor Technology, Theoretical Electrical Engineering
Term from 2019 to 2024
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 430166206
 
Final Report Year 2024

Final Report Abstract

Molecular communication (MC) is an emerging field of research at the intersection of science and engineering. MC is the prevalent means of communication in natural biological systems, e.g., between a presysynaptic neuron and a postsynaptic neuron in the synaptic cleft or between bacteria during quorum sensing. Engineered MC systems are expected to enable communication between nanomachines and to facilitate interaction with biological systems. This will pave the way for several medical, agricultural, and industrial applications including targeted drug delivery, environmental monitoring and nanoscale quality control. One of the most fascinating envisioned applications of MC is the Internet of BioNanoThings (IoBNT), facilitating detection and treatment of diseases inside the human body based on an in-body network of nanodevices and an external control unit. In MC, information is encoded in the properties of small particles. The development of the underlying communication and information theory and corresponding testbeds for experimental verification are still in their infancy. In this context, powerful and flexible analytical models and fast simulation methods for the spatio-temporal distribution of the information-carrying particles are of paramount importance. On the one hand, such models and methods are needed for the communication- and information-theoretical design and analysis of MC systems, and on the other hand, they are needed to guide the development of experiments. Unfortunately, the existing analytical models for MC systems are restricted to very simple MC channels, often relying on unrealistic simplifying assumptions, and the existing simulation methods are either accurate but slow or fast but not insightful, i.e., they offer only little insight into the impact of the various MC channels. Therefore, this project aimed to develop powerful and flexible analytical models for the spatio-temporal distribution of the information-carrying particles in MC systems based on a transfer function approach. In the course of the project, we were able to develop a framework for the analytical modeling of MC systems based on transfer functions. We were able to apply the approach to derive more realistic and efficient analytical models for a variety of relevant components of MC systems for which only approximate models or no models at all existed. Due to the application-oriented research approach and the publication of our developed models and concepts, the developed modeling approach is also used by other research groups and in follow-up studies. In total, the results of the project have been disseminated in 7 journal, 7 conference, and 1 book publications.

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