Markov State--Space Modeling and Channel Characterization for DNA-Based Molecular Communication
🤖 AI Summary
This work addresses inter-symbol interference and colored counting noise arising from reversible hybridization in DNA-based molecular communication, as well as the absence of a communication-oriented channel model tailored for microarray receivers. To this end, the authors propose a Markovian state-space framework based on voxelized reaction–diffusion dynamics that jointly characterizes molecular propagation, binding/unbinding kinetics, and channel memory effects. A block-structured transition matrix is introduced for the first time to derive an observation model for on-off keying signaling and to obtain a closed-form expression for the counting noise covariance. Building upon this foundation, a differential threshold detector and a finite-memory decision-feedback equalizer are designed. Simulations validate the theoretical analysis of noise correlation and demonstrate that the proposed receiver architecture achieves significant and complementary performance gains across diverse channel memory mechanisms.
Application Category
📝 Abstract
In this paper, we study DNA-based molecular communication with microarray-style reception under reversible hybridization, where the bound-state observation exhibits both inter-symbol interference and colored counting noise. To capture these effects in a communication-oriented form, we develop a Markov state-space framework based on a voxelized reaction--diffusion model, in which a block-structured transition matrix describes molecular transport and binding/unbinding dynamics. For the microarray specialization, this representation yields the channel impulse response, the equilibrium gain, and a settling-time-based characterization of the effective channel memory. Building on the resulting symbol-rate observation model for on--off keying, we derive a grouped-binomial counting model and obtain a closed-form expression for the covariance of the counting noise. Based on these statistics, we further develop a differential-threshold detector and a finite-memory decision-feedback equalizer. Numerical results validate the theoretical correlation behavior and show that the relative performance of the proposed receivers depends strongly on the channel-memory regime.
Problem
Research questions and friction points this paper is trying to address.
DNA-based molecular communication
inter-symbol interference
colored counting noise
channel characterization
reversible hybridization
Innovation
Methods, ideas, or system contributions that make the work stand out.
Markov state-space modeling
DNA-based molecular communication
reversible hybridization
colored counting noise
decision-feedback equalizer
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2024-01-29Citations: 1
