Molecular communication is a new and bio-inspired field, where chemical signals are used to transfer information instead of electromagnetic or electrical signals. In this paradigm, the transmitter releases chemicals or molecules and encodes information on some property of these signals such as their timing or concentration. The signal then propagates the medium between the transmitter and the receiver through different means such as diffusion, until it arrives at the receiver where the signal is detected and the information decoded. This new multidisciplinary field can be used for in-body communication, secrecy, networking microscale and nanoscale devices, infrastructure monitoring in smart cities and industrial complexes, as well as for underwater communications. Since these systems are fundamentally different from telecommunication systems, most techniques that have been developed over the past few decades to advance radio technology cannot be applied to them directly.
In this talk, we first explore some of the fundamental limits of molecular communication channels, evaluate how capacity scales with respect to the number of particles released by the transmitter, and the optimal input distribution. Finally, since the underlying channel models for some molecular communication systems are unknown, we demonstrate how techniques from machine learning and deep learning can be used to design components such as detection algorithms, directly from transmission data, without any knowledge of the underlying channel models.