Molecular communication (MC) is a novel communication paradigm envisioned to enable revolutionary future medical and biological use cases such as in-body networks for the diagnosis and treatment of diseases. MC is based on the transport of molecules for information exchange and represents a very energy-efficient and bio-compatible communication mechanism on the centimeter to nanometer scale. The communication nodes can be very small as they will be based on artificial cells or other types of tiny nano-machines.

In order to realize complex applications, such as targeted drug delivery or the detection and localization of infections and tumors, nano-machines must cooperate and communicate. The specific properties and mechanisms in biological environments and at very small scales lead to several challenges:

• Novel channel models and conditions based on diffusion and flow of molecules.
• Extremely slow speeds compared to electromagnetic waves.
• Highly stochastic behavior of the molecules.
• Low capability of future nano-machines, not able to conduct complex computations or sophisticated algorithms.

Therefore, research on MC networks is crucial to enable a future internet of bio-nano things (IoBNT) integrating classical and molecular networks.

The student will conduct research on the development and evaluation of network protocols and algorithms for MC networks in the IoBNT. This could include areas such as multiple access, resource management schemes, or other types of network optimization. They will get the opportunity to work with one or multiple of the following tools:

• Analytical models for diffusion and flow, as well as traditional network performance analysis.
• Simulations tools for communication networks, chemical reaction networks, and fluid dynamics.
• Experimental platforms for MC based on state-of-the-art microfluidic equipment.

• Interest in unconventional future communication networks.

• Willingness to approach and learn about new topics.
• Good knowledge of a popular programming language like Python and/or Matlab.

·        Optional:

• Prior knowledge of fluid dynamics simulations (e.g. OpenFOAM).
• Experience with microcontroller programming (preferably Arduino).
• Experience with CAD, 3D printing, or soldering

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Molecular communication (MC) is a novel communication paradigm envisioned to enable revolutionary future medical and biological use cases such as in-body networks for the diagnosis and treatment of diseases. MC is based on the transport of molecules for information exchange and represents a very energy-efficient and bio-compatible communication mechanism on the centimeter to nanometer scale. The communication nodes can be very small as they will be based on artificial cells or other types of tiny nano-machines.

In order to realize complex applications, such as targeted drug delivery or the detection and localization of infections and tumors, nano-machines must cooperate and communicate. The specific properties and mechanisms in biological environments and at very small scales lead to several challenges:

• Novel channel models and conditions based on diffusion and flow of molecules.
• Extremely slow speeds compared to electromagnetic waves.
• Highly stochastic behavior of the molecules.
• Low capability of future nano-machines, not able to conduct complex computations or sophisticated algorithms.

Therefore, research on MC networks is crucial to enable a future internet of bio-nano things (IoBNT) integrating classical and molecular networks.

In this thesis, the student will work on the topic of chemical reaction networks (CRNs), which represent a possible substrate for computations and programmability in biological systems. A CRN is built from a number of coupled chemical reactions and is designed to turn a certain concentration of input molecules into a concentration of output molecules.

The student will be tasked with implementing a CRN that approximates a real signal processing algorithm, namely successive interference cancellation (SIC). SIc could be used, for example, to realize non-orthogonal multiple access schemes in a larger MC network. 

The CRN will be designed conceptually and implemented using Python. Then, the CRN will be evaluated rigorously using both deterministic solvers based on differential equations, as well as stochastic simulations that take into account individual random molecule interactions.

Molecular communication (MC) is a novel communication paradigm envisioned to enable revolutionary future medical and biological use cases such as in-body networks for the diagnosis and treatment of diseases. MC is based on the transport of molecules for information exchange and represents a very energy-efficient and bio-compatible communication mechanism on the centimeter to nanometer scale. The communication nodes can be very small as they will be based on artificial cells or other types of tiny nano-machines.

In order to realize complex applications, such as targeted drug delivery or the detection and localization of infections and tumors, nano-machines must cooperate and communicate. The specific properties and mechanisms in biological environments and at very small scales lead to several challenges:

• Novel channel models and conditions based on diffusion and flow of molecules.
• Extremely slow speeds compared to electromagnetic waves.
• Highly stochastic behavior of the molecules.
• Low capability of future nano-machines, not able to conduct complex computations or sophisticated algorithms.

Therefore, research on MC networks is crucial to enable a future internet of bio-nano things (IoBNT) integrating classical and molecular networks.

In this internship, the student will work with experimental data from an MC testbed to implement, evaluate, and improve detection algorithms. The considered algorithms range from simple to complex, including symbol-by-symbol detection, sequence detection, matched filters, etc. The goal is to identify the strengths and weaknesses of various algorithms with respect to the characteristics of the MC signal at the receiver.

Implement a particle-based simulation framework for molecular communication networks:

Use e.g. Python or MATLAB

Main target scenario: cylindrical tube with multiple transmitters and receivers

Effects: Diffusion, laminar/uniform flow -> output: particle trajectories

Enable different types of transmitters and receivers (e.g. point, cross-sectional distribution, volume) Flexibility in placement of TX and RX and shape of the initial molecules distribution  

Implement and evaluate a non-orthogonal multiple access scheme based on the distance and the emitted number of molecules from each TX