Uncertainty Quantification in Molecular Signals using Polynomial Chaos Expansion

Molecular signals are abundant in engineering and biological contexts, and undergo stochastic propagation in fluid dynamic channels. The received signal is sensitive to a variety of input and channel parameter variations. Currently we do not understand how uncertainty or noise in a variety of parameters affect the received signal concentration, and nor do we have an analytical framework to tackle this challenge. In this paper, we utilize Polynomial Chaos Expansion (PCE) to show to uncertainty in parameters propagates to uncertainty in the received signal. In demonstrating its applicability, we consider a Turbulent Diffusion Molecular Communication (TDMC) channel and highlight which parameters affect the received signals. This can pave the way for future information theoretic insights, as well as guide experimental design

Bacterial relay for energy efficient molecular communications

In multi-cellular organisms, molecular signaling spans multiple distance scales and is essential to tissue structure and functionality. Molecular communications is increasingly researched and developed as a key subsystem in the Internet-of-Nano-Things paradigm. While short range microscopic diffusion communications is well understood, longer range channels can be inefficient and unreliable. Static and mobile relays have been proposed in both conventional wireless systems and molecular communication contexts. In this paper, our main contribution is to analyze the information delivery energy efficiency of bacteria mobile relays. We discover that these mobile relays offer superior energy efficiency compared with pure diffusion information transfer over long diffusion distances. This paper has widespread implications ranging from understanding biological processes to designing new efficient synthetic biology communication systems

Molecular signal tracking and detection methods in fluid dynamic channels

This method and data paper sets out the macro-scale experimental techniques to acquire fluid dynamic knowledge to inform molecular communication performance and design. Fluid dynamic experiments capture latent features that allow the receiver to detect coherent signal structures and infer transmitted parameters for optimal decoding. This paper reviews two powerful fluid dynamical measurement methodologies that can be applied beneficially in the context of molecular signal tracking and detection techniques. The two methods reviewed are Particle Image Velocimetry (PIV) and Planar Laser-Induced Fluorescence (PLIF). Step-by-step procedures for these techniques are outlined as well as comparative evaluation in terms of performance accuracy and practical complexity is offered. The relevant data is available on IEEE DataPort to help in better understanding of these method

Radiation absorption noise for molecular information transfer

Molecular signaling is ubiquitous across scales in nature and finds useful applications in precision medicine and heavy industry. Characterizing noise in communication systems is essential to understanding its information capacity. To date, research in molecular nano communication (MNC) primarily considers the molecular dynamics within the medium, where various forms of stochastic effects generate noise. However, in many real-world scenarios, external effects can also influence molecular dynamics and cause noise. Here, the noise due to the temperature fluctuations from incident electromagnetic (EM) radiation is considered, with applications ranging from cell signaling to chemical engineering. EM radiation and subsequent molecular absorption cause temperature fluctuations which affect molecular dynamics and can be considered as an exogenous noise source for MNC. In this paper, the probability density function of the radiation absorption noise (RAN) is analyzed and to demonstrate applicability, we include characteristics of different tissues of the human body. Furthermore, the closed-form expression of error probability (EP) for MNC under the radiation noise is derived. Numerical analysis is demonstrated on different tissues of the human body: skin, brain, and blood, as well as the polarization factor of incident EM radiation is demonstrated. The coupling relationship between the radiation frequency and the intrinsic impedance of the human body on the PDF of radiation absorption noise is presented. This is useful for understanding how mutual information changes with external radiation source

Channel Modeling for Diffusive Molecular Communication - A Tutorial Review

Molecular communication (MC) is a new communication engineering paradigm where molecules are employed as information carriers. MC systems are expected to enable new revolutionary applications such as sensing of target substances in biotechnology, smart drug delivery in medicine, and monitoring of oil pipelines or chemical reactors in industrial settings. As for any other kind of communication, simple yet sufficiently accurate channel models are needed for the design, analysis, and efficient operation of MC systems. In this paper, we provide a tutorial review on mathematical channel modeling for diffusive MC systems. The considered end-to-end MC channel models incorporate the effects of the release mechanism, the MC environment, and the reception mechanism on the observed information molecules. Thereby, the various existing models for the different components of an MC system are presented under a common framework and the underlying biological, chemical, and physical phenomena are discussed. Deterministic models characterizing the expected number of molecules observed at the receiver and statistical models characterizing the actual number of observed molecules are developed. In addition, we provide channel models for time-varying MC systems with moving transmitters and receivers, which are relevant for advanced applications such as smart drug delivery with mobile nanomachines. For complex scenarios, where simple MC channel models cannot be obtained from first principles, we investigate simulation-driven and experimentally-driven channel models. Finally, we provide a detailed discussion of potential challenges, open research problems, and future directions in channel modeling for diffusive MC systems.Comment: 40 pages; 23 figures, 2 tables; this paper is submitted to the Proceedings of IEE

Channel modeling for diffusive molecular communication - a tutorial review

Molecular communication (MC) is a new communication engineering paradigm where molecules are employed as information carriers. MC systems are expected to enable new revolutionary applications such as sensing of target substances in biotechnology, smart drug delivery in medicine, and monitoring of oil pipelines or chemical reactors in industrial settings. As for any other kind of communication, simple yet sufficiently accurate channel models are needed for the design, analysis, and efficient operation of MC systems. In this paper, we provide a tutorial review on mathematical channel modeling for diffusive MC systems. The considered end-to-end MC channel models incorporate the effects of the release mechanism, the MC environment, and the reception mechanism on the observed information molecules. Thereby, the various existing models for the different components of an MC system are presented under a common framework and the underlying biological, chemical, and physical phenomena are discussed. Deterministic models characterizing the expected number of molecules observed at the receiver and statistical models characterizing the actual number of observed molecules are developed. In addition, we provide channel models for timevarying MC systems with moving transmitters and receivers, which are relevant for advanced applications such as smart drug delivery with mobile nanomachines. For complex scenarios, where simple MC channel models cannot be obtained from first principles, we investigate simulation-driven and experiment-driven channel models. Finally, we provide a detailed discussion of potential challenges, open research problems, and future directions in channel modeling for diffusive MC systems

Molecular Channel Fading Due to Diffusivity Fluctuations

Molecular Communications via Diffusion (MCvD) is sensitive to environmental changes such as the diffusion coefficient (mass diffusivity). The diffusivity is directly related to a number of parameters including the ambient temperature, which varies slowly over time. Whilst molecular noise models have received significant attention, channel fading has not been extensively considered. Using experimental data, we show that the ambient temperature varies approximately according to a Normal distribution. As a result, we analytically derive the fading distribution and validate it using numerical simulations. We further derive the joint distribution of the channel gain and the additive noise, and examine the impact of such interactions on the ISI distribution, which is shown to conform to a Generalised Extreme Value (GEV) distribution

Experimental and computational study of molecular communication in turbulent fluid environments

Molecular communication (MC) is a type of communication and networking in the electromagnetic (EM)-denied environments. MC is concerned with information transfer by preserving information in the structure of chemical flow through molecular diffusion, advection or reaction. Hence, the information transmission in MC is closely associated with the physics of fluid dynamics. The mechanism of MC, i.e., using chemical substances for information exchange, is prevalent in nature among organisms at various length scales, from intra-cell signaling and bacterial communication to airborne and waterborne pheromone signals. At nano-scale the physical conditions are such that the main mechanism of transport is mass diffusion. Therefore fluid turbulence, for which other transport mechanisms are relevant, have hitherto hardly been considered at all in the context of MC. Nevertheless, MC is obviously not restricted to nano-scales, as demonstrated by insect and crustacean pheromone signaling. Here turbulence does become a crucial issue affecting the reliability of the message transfer. The goal of this thesis is to draw on turbulence theory to assess implications of relevance to MC at macro scale. The results show that in turbulent channels, viscous shear stresses hinder a reliable transfer of the molecular information between the transmitter and the receiver which results in severe inter-symbol-interference (ISI). In order to mitigate the ISI in turbulent channels, vortex ring are proposed as coherent structures representing a means for modulating information symbols onto them. Each vortex ring can propagate approximately 100× the diameter of the transmission nozzle without losing its compact shape. It is shown that by maintaining a coherent signal structure, the signal-to-inference (SIR) ratio is higher over conventional puffs. Moreover, the results show that the received signals of the same transmitted symbols vary due to the presence of the underlying noise in turbulent channels. To understand the behaviour of the noise in turbulent channels, both of the additive and jitter noises distributions characterised statistically, and a new channel model is proposed. Thereafter, this channel model is used to quantify the mutual information in turbulent channels. Finally, the waterborne chemical plumes are investigated as a paradigm for a means of molecular communication at macro scales. Results from the Richardson’s energy cascade theory are applied and interpreted in the context of MC to characterise an information cascade and the information dissipation rate. The results show that the information dissipation rate decreases with increasing the Reynolds number and distance d from transmitter. This may appear counter intuitive because stronger turbulence levels at higher Reynolds numbers increases energy dissipation rates. However, increased turbulence leads to more efficient scalar mixing and, therewith, the power of the molecular signal quickly reduces to low levels. Accordingly the information dissipation rate necessarily reduces due to the remaining low information content available