Diffusion-based physical channel identification in molecular nanonetworks

Abstract

Nanotechnology is the engineering of functional systems at the molecular/atomic level. Nanomachines are expected to be very simple devices, but nanonetworks, the association of nanomachines, are expected to increase their capabilities, allowing them to share information in order to perform more complex tasks and increase their range of operation. How nanomachines will communicate is currently under In this work, the diffusion-based MC channel is explored in order to extract its main communication metrics, such as attenuation and delay with respect to frequency and distance. The LTI property is proven to be a valid assumption for normal diffusionbased single/multi-transmitter scenarios. Different pulse-based modulation techniques are compared by means of throughput, operation range, energy requirements and ISI, and the optimal pulse shape for these modulations is provided. Finally, interferences are evaluated in a broadcast communication scenario and diffusion-based noise is observed and assessed with reference to already proposed stochastic models.The exploration of the physical diffusion-based communication channel is based on simulations. N3Sim, a simulation framework for the general case of diffusion communication, is presented and used for the simulations.We aim to contribute with this simulator and this study to continue the development of an appropriate channel model tailored to the physical layer singularities of

Maximum-Likelihood Estimator of Clock Offset between Nanomachines in Bionanosensor Networks

“…Using the equation $\mu =\frac{d}{v}$ mentioned in Section 2, we choose the average propagation delay of the information molecules from 1 to 5 μs which is within the feasible range. The diffusion coefficient D is set to 1–10 μm 2 /μs, which is used in [36,40]. According to the equation $\lambda =\frac{d^2}{2D}$ mentioned in Section 2, we choose the shape parameter $\lambda =1$, which is within the feasible range.…”

Modeling CD40-Based Molecular Communications in Blood Vessels

“…Particle positions are assumed to lie on lattice points; in addition, nano machine positions are assumed to be fixed. The Java-based simulator illustrated in [20]- [22], called N3Sim, emulates a two-dimensional particle Brownian diffusion model, with 3D extensions in specific conditions. Propagation phenomena includes inertia forces and particle collisions.…”

Establishing A Molecular Communication Channel for Nano Networks

“…Garralda et al have used N3Sim simulator in [16] which simulates propagation of individual molecule using BM and verifies the results using Fick's Laws. Yet another attempt to combine deterministic and stochastic approaches can be found in [17] where propagation is modelled as diffusion followed by particle location displacement which is stochastic by nature.…”

“…Therefore, the higher the diffusion coefficient the narrower the received pulses will be and in turn, may provide higher data rate in case of timing channels [23]. Fig.4 Transmission and reception of two pulses and their addition to prove linearity of molecular channel [16] Fig.5 Transmission and reception of a single pulse and its time shifted version to prove temporal invariance of molecular channel [16] Garralda et al have shown in [16] that a diffusion based MC channel in a linear time invariant channel, in single as well as multi transmitter scenarios. It is very clear from figure 4 that the superposition principle holds true in the example of 2 different pulses as well as their addition in a diffusive channel.…”