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Channel Modeling for Diffusive Molecular Communication - A Tutorial Review (1812.05492v1)

Published 13 Dec 2018 in cs.ET

Abstract: 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.

Citations (261)

Summary

  • The paper introduces comprehensive channel models for diffusive molecular communication by merging deterministic and stochastic approaches.
  • It establishes channel impulse responses using advection-reaction-diffusion equations and particle-based simulations.
  • The review outlines applications in biomedical and AI-driven nanonetworks, emphasizing smart drug delivery and environmental monitoring.

Channel Modeling for Diffusive Molecular Communication -- A Tutorial Review: An Expert Overview

In the domain of molecular communication (MC), where the conveyance of information is facilitated by molecules, channel modeling serves as a crucial component. The paper Channel Modeling for Diffusive Molecular Communication -- A Tutorial Review meticulously examines and extends various channel models necessary for the comprehensive design, analysis, and operation of MC systems. It explores the interdisciplinary integration of biological, chemical, and physical principles to model the communication channels, emphasizing deterministic and statistical frameworks within this novel communication paradigm. Here, we delve into the core aspects of this comprehensive survey, its numerical results, and its implications concerning the future of AI and nanonetworks.

Core Framework of Molecular Communication

Molecular communication capitalizes on the natural chemical processes to facilitate communication between nanodevices, finding applications in biomedical, environmental, and industrial fields. This paper provides a formidable tutorial on the foundational channel models needed for MC systems, primarily focusing on diffusive propagation mechanisms frequently observed in nature. The communication through molecular diffusion is modeled through advection-reaction-diffusion equations, which are solved for various boundary and initial conditions pertinent to different MC environments.

Methodology: Combining Deterministic and Stochastic Models

The discussion is divided into sections focusing on diffusion, advection, and chemical reactions. Each physical phenomenon is rigorously modeled to yield channel impulse responses (CIRs) which convey the likelihood of molecule detection at the receiver. The methodology employed includes deterministic models characterizing the mean behavior of molecule transport and reception alongside stochastic models that capture variance-induced uncertainties compelling in the biological milieu.

Through a profound exploration, deterministic models shed light on mean concentration dynamics, solving Fick's law-based equations. In contrast, stochastic facets tap into particle-based simulation for reaction-diffusion systems, modeling uncertainty through stochastic simulation tactics and statistical tools like Poisson and Gaussian approximations.

Numerical Insights and Novel Contributions

The paper distinguishes itself by contextualizing advancements made in channel modeling post earlier surveys; for instance, incorporating advective flow and reactions in channel models for enriched accuracy. Significant contributions include the presentation of time-varying CIRs for dynamic systems involving mobile transmitters and receivers, pertinent for mobile nanomachines in smart drug delivery. They further unveil a unified signal representation aiding both counting and timing-based reception, comprehensive enough to facilitate interference and intersymbol interference considerations.

Implications for AI and Nanonetworks

Practically, these models cater to the communication design in constrained environments, such as inside the human body for medical applications or monitoring hazardous environments, albeit extending to larger macroscale operations like pollution monitoring. The intersection of AI in MC inch forwards with the possibility of creating intelligent systems autonomously operating through biological circuits, using the established channel models for optimizing information transfer.

Theoretically, ongoing modeling improvements stand to contribute significantly. Suggested directions include further examining chemical pathways for improved reception models and considering real-world experimental validations of these theoretical models. Such efforts not only enhance MC system design but also dovetail into broader AI applications, potentially evolving towards AI-driven autonomous molecular machines.

Conclusion: Groundwork for Advanced Exploration

This paper achieves compiling a formidable base of knowledge significantly broadening the quintessential understanding required for MC channel modeling. Beyond the numerical prowess and theoretical depths, the tutorial review serves as a stepping stone toward enhanced explorations into scalable, reliable, and insightful molecular communication networks. The future developments outlined, driven by these findings, reinforce MC as an emerging frontier coupling interdisciplinary sciences with practical AI leanings, destined to stimulate fascinating applications and innovations within the rapidly evolving landscape of nanonetworks and communication systems at large.