Abstract:
As a widely acknowledged information transfer method in the nanonetworking domain, Molecular Communication via Diffusion (MCvD) presents many advantages as well as challenges. In order to assess the capabilities and restrictions of MCvD, a thorough understanding of the reception process and the achievable rate holds utmost importance. With a reflective spherical transmitter and a fully absorbing spherical receiver, the network setup becomes more realistic, but analytical derivations become increasingly difficult. In this thesis, we propose two novel heavy-tail distributions to statistically model the distribution of the first passage time of messenger molecules (MM), conduct Kolmogorov-Smirnov goodness of fit tests for model validation, and examine the modeling performance under diverse deployment parameters. We also in vestigate MM absorption probability, Signal-to-Interference Ratio, and the advantages of using a reflective transmitter. Since the heavy-tailed signal causes Inter-Symbol In terference (ISI), the MCvD channel has memory, and Shannon’s capacity formula for memoryless channels is inapplicable. To this end, we propose an accurate ISI-aware model of demodulation and bit error probabilities for Binary Concentration Shift Key ing modulated MCvD, carry out goodness of fit tests, and prove that the literature’s assumption of a single symbol duration memory is overly optimistic. Furthermore, we adapt the general formulation of the achievable rate for ergodic finite state ISI channels to MCvD and investigate the effect of deployment parameters, demodulation threshold, and input distribution on the achievable rate. We also present preliminary findings on estimating the achievable rate with a neural network. Finally, we apply the biological concept of protrusions to MCvD, in order to physically reduce ISI and study the effect of protrusion deployment parameters on the achievable rate.
