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Molecular communication via diffusion (MCvD) is one of the paradigms in nanonetworks. Finding an approximation or analytical solution for the fraction of the received molecules to analyze the channel behavior is essential in molecular communication. Current studies propose approximations to model simple channel topologies, i.e. topologies with few nodes. To model complex channel topologies, time-consuming particle-based Monte Carlo simulations are used. We propose MCvD-Transformer to avoid the time-consuming simulations and estimate the fraction of the received molecules for complex topologies. MCvD-Transformer is trained via instances containing various topologies and time-dependent estimations for a fraction of received molecules estimated by particle-based Monte Carlo simulations. Finally, MCvD-Transformer is compared with both the studies in the literature and the simulations. As a result, MCvD-Transformer performs better than literature studies in terms of root mean squared error and maximum normalized absolute error metrics on our test dataset. Therefore, the proposed model is more accurate in modeling complex MCvD topologies than the current state of the art without time-consuming simulations. Additionally, it is expected to be a benchmark for the works that focus on complex MCvD topologies. [Display omitted] • Estimation of the expected fraction of the received molecules until time t for complex and generic 3D MCvD channel topologies using ANN. • Elimination of time-consuming simulations. • Being a benchmark for future studies focusing on complex topologies. [ABSTRACT FROM AUTHOR]

In this paper, spatial transmission techniques in the area of multiple-input multipleoutput (MIMO) diffusion-based molecular communications (DBMC) are investigated. For transmitter-side spatial coding, Alamouti-type coding and repetition MIMO coding are analyzed. At the receiver-side, selection diversity and equal-gain combining are studied as combining strategies. Throughout the numerical analysis, a symmetrical 2 × 2 MIMO-DBMC system is assumed. Furthermore, a trained artificial neural network is utilized to acquire the channel impulse responses. The numerical analysis demonstrates that there is no spatial diversity gain in the DBMC system under investigation, but that it is possible to achieve an array gain instead. In addition, it is shown that for MIMO-DBMC systems repetition MIMO coding is superior to Alamouti-type coding.

Molecular communication via diffusion in vessel-like environment targets critical applications such as the detection of abnormal and unhealthy cells. In this letter, we derive the analytical formulation of the channel model for diffusion dominated movement, considering ring-shaped (i.e., patch) observing receivers, and Poiseuille flow with the aim of localization of the transmitter cell. Then, we derive formulations using this channel model for two different application scenarios. We assume that the emission start time is known in the first scenario and unknown in the second one. We successfully localize the transmitter cell using a single receiver for the first scenario, whereas two receivers are used to localize the transmitter cell in the second scenario. At last, the devised analytical framework is validated with simulations. [ABSTRACT FROM AUTHOR]