Markov State--Space Modeling and Channel Characterization for DNA-Based Molecular Communication
This work addresses communication challenges in molecular systems for applications like nanonetworks, but it is incremental as it builds on existing models with specific adaptations.
The paper tackles the problem of DNA-based molecular communication with reversible hybridization, which suffers from inter-symbol interference and colored counting noise, by developing a Markov state-space model to characterize the channel and designing receivers that show performance varying with channel-memory regimes.
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.