We consider molecular communication networks consisting of transmitters and receivers distributed in a fluidic medium. In such networks, a transmitter sends one or more signaling molecules, which are diffused over the medium, to the receiver to realize the communication. In order to be able to engineer synthetic molecular communication networks, mathematical models for these networks are required. This paper proposes a new stochastic model for molecular communication networks called reaction-diffusion master equation with exogenous input (RDMEX). The key idea behind RDMEX is to model the transmitters as time series of signaling molecule counts, while diffusion in the medium and chemical reactions at the receivers are modeled as Markov processes using master equation. An advantage of RDMEX is that it can readily be used to model molecular communication networks with multiple transmitters and receivers. For the case where the reaction kinetics at the receivers is linear, we show how RDMEX can be used to determine the mean and covariance of the receiver output signals, and derive closed-form expressions for the mean receiver output signal of the RDMEX model. These closed-form expressions reveal that the output signal of a receiver can be affected by the presence of other receivers. Numerical examples are provided to demonstrate the properties of the model.
Molecular dynamics is commonly used in simulation of molecular communication networks. Many examples of simulators exist, especially for natural molecular communication networks, see @cite_6 for a recent overview. For synthetic networks, a recent example is @cite_16 . By analysing the molecular dynamics of transmitters and receivers, @cite_3 characterises the noise in transmitters and receivers as, respectively, sampling and counting noise.
@cite_16 @cite_3 @cite_6
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Abstract A number of nanomachines that cooperatively communicate and share molecular information in order to achieve specific tasks is envisioned as a nanonetwork. Due to the size and capabilities of nanomachines, the traditional communication paradigms cannot be used for nanonetworks in which network nodes may be composed of just several atoms or molecules and scale on the orders of few nanometers. Instead, molecular communication is a promising solution approach for the nanoscale communication paradigm. However, molecular communication must be thoroughly investigated to realize nanoscale communication and nanonetworks for many envisioned applications such as nanoscale body area networks, and nanoscale molecular computers. In this paper, a simulation framework (NanoNS) for molecular nanonetworks is presented. The objective of the framework is to provide a simulation tool in order to create a better understanding of nanonetworks and facilitate the development of new communication techniques and the validation of theoretical results. The NanoNS framework is built on top of core components of a widely used network simulator (ns-2). It incorporates the simulation modules for various nanoscale communication paradigms based on a diffusive molecular communication channel. The details of NanoNS are discussed and some functional scenarios are defined to validate NanoNS. In addition to this, the numerical analyses of these functional scenarios and their experimental results are presented. The validation of NanoNS is shown via comparative evaluation of these experimental and numerical results. Molecular communication (MC) is a promising bio-inspired paradigm, in which molecules are used to encode, transmit and receive information at the nanoscale. Very limited research has addressed the problem of modeling and analyzing the MC in nanonetworks. One of the main challenges in MC is the proper study and characterization of the noise sources. The objective of this paper is the analysis of the noise sources in diffusion-based MC using tools from signal processing, statistics and communication engineering. The reference diffusion-based MC system for this analysis is the physical end-to-end model introduced in a previous work by the same authors. The particle sampling noise and the particle counting noise are analyzed as the most relevant diffusion-based noise sources. The analysis of each noise source results in two types of models, namely, the physical model and the stochastic model. The physical model mathematically expresses the processes underlying the physics of the noise source. The stochastic model captures the noise source behavior through statistical parameters. The physical model results in block schemes, while the stochastic model results in the characterization of the noises using random processes. Simulations are conducted to evaluate the capability of the stochastic model to express the diffusion-based noise sources represented by the physical model. One of the fundamental motivations underlying computational cell biology is to gain insight into the complicated dynamical processes taking place, for example, on the plasma membrane or in the cytosol of a cell. These processes are often so complicated that purely temporal mathematical models cannot adequately capture the complex chemical kinetics and transport processes of, for example, proteins or vesicles. On the other hand, spatial models such as Monte Carlo approaches can have very large computational overheads. This chapter gives an overview of the state of the art in the development of stochastic simulation techniques for the spatial modelling of dynamic processes in a living cell.