Molecular Communication via Diffusion (MCvD) is a prominent small-scale technology, which roots from the nature. With solid analytical foundations on channel response and advanced modulation techniques, molecular single-input-single-output (SISO) systems are one of the most studied molecular networks in the literature. However, the literature is yet to provide sufficient analytical channel modeling on molecular multiple-output systems with fully absorbing receivers, one of the common applications in the area. In this paper, a channel model for molecular single-input-multiple-output (SIMO) systems is proposed for estimating the channel response of such systems. With the model’s recursive nature, the closed-form solution of the channel response of molecular 2-Rx SIMO systems is analytically derived. A simplified model with lower complexity is also presented at a cost of slightly less accurate channel estimation. The models are extended to the molecular SIMO systems with more than two receivers. The performance of the methods are evaluated for several topologies with different parameters, and the accuracy of the model is verified by comparing to computer-simulated channel estimations in terms of quantitative error metrics such as root-mean-squared error. The performance of the simplified model is verified by the amount of deviation, indicating sufficient channel modeling performance with reduced computational power.

A basic property of any diffusion-based molecular communication system is the geometry of the enclosing container. In particular, the geometry influences the system's behavior near the boundary and in all existing modulation schemes governs receiver design. However, it is not always straightforward to characterize the geometry of the system. This is particularly the case when the molecular communication system operates in scenarios where the geometry may be complex or dynamic. In this article, we propose a new scheme-called equilibrium signaling-which is robust to uncertainties in the geometry of the fluid boundary. In particular, receiver design only depends on the relative volumes of the transmitter or receiver, and the entire container. Our scheme relies on reversible reactions in the transmitter and the receiver, which ensure the existence of an equilibrium state into which information is encoded. In this case, we derive near optimal detection rules and develop a simple and effective estimation method to obtain the container volume. We also show that equilibrium signaling can outperform classical modulation schemes, such as concentration shift keying, under practical sampling constraints imposed by biological oscillators.

In this paper, a transfer function that models Molecular Communication via Diffusion (MCvD) between a point transmitter and a fully absorbing circular receiver in an unbounded 2-D environment is analytically derived. The analytical derivation of the transfer function of the 2-D channel in MCvD is proposed for the first time in the literature. The transfer function is obtained by solving the molecule concentration equations around the receiver considering diffusion dynamics, using some initial and boundary conditions, and some reasonable assumptions. Analytical results are compared with the results of Monte Carlo simulations.

This paper is on simultaneous exploration and topological map building in completely unknown environments. We propose an approach that consists of a continuous loop of local decision-making (where potential search directions are added to the current node) and global decision-making (where the robot moves along one of these directions and adds possibly a new node to the map ) using the previously proposed bubble space representation. As the robot switches between the two in a continual manner, it creates a coarse graph representation of the environment. There is a node for every explored location with a set of associated edges that either indicate potential search directions or connect to the other explored locations. The so-far constructed topological map contains all the required information in regards to deciding where to move with comparatively low memory requirements. Experimental results with an extensive real data set demonstrate that large terrains can be explored and mapped efficiently using this approach.