|dc.description.abstract||Molecular communication (MC) is a promising bio-inspired paradigm for the exchange of information among nanotechnology-enabled devices. These devices, called nanomachines, are expected to have the ability to sense, compute and actuate, and interconnect into networks, called nanonetworks, to overcome their individual limitations and benefit from collaborative efforts. MC realizes the exchange of information through the transmission, propagation, and reception of molecules, and it is proposed as a feasible solution for nanonetworks. This idea is motivated by the observation of nature, where MC is successfully adopted by cells for intracellular and intercellular communication. MC-based nanonetworks have the potential to be the enabling technology for a wide range of applications, mostly in the biomedical, but also in the industrial and surveillance fields.
The focus of this Ph.D. thesis is on the most fundamental type of MC, i.e., diffusion-based MC, where the propagation of information-bearing molecules between a transmitter and a receiver is realized through free diffusion in a fluid. The objectives of the research presented in this thesis are to analyze the MC paradigm from the point of view of communication engineering and information theory, and to provide solutions to the modeling and design of MC-based nanonetworks. First, a physical end-to-end model is realized to study each component in a basic diffusion-based MC system design, as well as the overall system, in terms of gain and delay. Second, the noise sources affecting a diffusion-based MC are identified and statistically modeled. Third, upper/lower bounds to the capacity are derived to evaluate the information-theoretic performance of diffusion-based MC. Fourth, a stochastic analysis of the interference when multiple transmitters access the diffusion-based MC channel is provided. Fifth, as a proof of concept, a design of a diffusion-based MC system built upon genetically-engineered biological circuits is analyzed. This research provides fundamental results that establish a basis for the modeling, design, and realization of future MC-based nanonetworks, as novel technologies and tools are being developed.||