Improving Receiver Performance of Diffusive Molecular Communication with Enzymes (1305.1926v4)

Abstract: This paper studies the mitigation of intersymbol interference in a diffusive molecular communication system using enzymes that freely diffuse in the propagation environment. The enzymes form reaction intermediates with information molecules and then degrade them so that they cannot interfere with future transmissions. A lower bound expression on the expected number of molecules measured at the receiver is derived. A simple binary receiver detection scheme is proposed where the number of observed molecules is sampled at the time when the maximum number of molecules is expected. Insight is also provided into the selection of an appropriate bit interval. The expected bit error probability is derived as a function of the current and all previously transmitted bits. Simulation results show the accuracy of the bit error probability expression and the improvement in communication performance by having active enzymes present.

• The paper introduces an enzyme-catalyzed reaction-diffusion model to mitigate intersymbol interference in molecular communication systems.
• It validates the analytical model through simulations, showing results that double data rates while reducing bit error probabilities.
• The approach optimizes bit interval selection and offers a simplistic detection scheme, paving the way for efficient bio-inspired nanonetworks.

Improving Receiver Performance of Diffusive Molecular Communication with Enzymes

This paper presents a paper on mitigating intersymbol interference (ISI) in diffusive molecular communication systems through the use of enzymes in the propagation environment. The research focuses on designing bio-inspired nanonetworks, where information molecules are released by a transmitter and detected by a receiver through free diffusion, a common natural propagation method.

Overview

The authors introduce an innovative approach by integrating enzymes, which are catalytic biomolecules, into the communication environment. These enzymes selectively react with information molecules to form reaction intermediates, eventually degrading them. This mechanism reduces ISI from previous transmissions and subsequently permits increased data rates by minimizing the interference that plagues such systems. Traditional approaches typically ignored ISI or compensated for it by limiting transmission rates, but this enzyme-catalyzed approach offers a promising alternative.

Key Contributions

1. Formulation of a Reaction-Diffusion Model: A comprehensive reaction-diffusion model using Michaelis-Menten kinetics is presented to capture the interaction of information molecules with enzymes. This model allows for the derivation of a lower bound on the expected number of information molecules at the receiver, taking into account the continuous degradation of these molecules by enzymes.
2. Analytical and Simulation Results: The paper devises and validates analytical expressions for the expected bit error probability, factoring in ISI from all previous transmissions. Comparisons between this analytical model and extensive simulations display high concordance, confirming the utility of the proposed model.
3. Optimization of Bit Interval: By evaluating the decay of the molecular signal, the authors provide insights into choosing an appropriate bit interval, which balances the transmission rate and error probability.
4. Simplistic Detection Scheme: An elementary detector scheme is analyzed, where the receiver samples the molecular concentration at the point of expected maximum concentration. The scheme’s performance, alongside proposed threshold-based decisions, offers insights on practical implementation.

Numerical Findings

The results from simulations mirror the theoretical predictions, highlighting how enzyme presence significantly suppresses ISI. Notably, data rates potentially double with enzyme activity, while maintaining or improving upon existing error rates seen in enzyme-absent models. For instance, using System 1 parameters, the model projects significant improvements in error performance and data throughput with enzymes compared to enzyme-free communication.

Implications and Future Work

The groundwork established by this research extends the boundaries of bio-inspired computing, paving pathways toward efficient molecular networks leveraging naturally occurring enzymatic reactions. These findings could influence future biomedical applications, autonomously positioned sensors, and beyond. Future developments may explore multi-user interference minimization, adaptive enzymatic systems for complex environments, and real-world deployment scenarios for bio-integrative communication systems.

This research underscores the importance of biomimicry in navigating molecular communication challenges. By employing biochemical reactions, new avenues open in the domain of nanonetworks to overcome traditional limitations dynamically, showcasing the vast potential of leveraging biological systems in protocol design and network architectures.