Markov State--Space Modeling and Channel Characterization for DNA-Based Molecular Communication

TL;DR

This paper develops a Markov state-space model for DNA-based molecular communication, capturing inter-symbol interference and noise, and proposes detection and equalization methods validated through numerical simulations.

Contribution

It introduces a novel voxelized reaction-diffusion Markov model for DNA communication channels, including new statistical characterizations and receiver designs.

Findings

The model accurately predicts channel behavior and noise correlation.

Proposed detectors improve communication performance under different channel memory conditions.

Numerical results confirm the effectiveness of the theoretical models and receiver algorithms.

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.