Molecular ISAC via Markov State-Space Modeling: Joint Distance Sensing and Data Detection

TL;DR

This paper introduces a molecular ISAC framework using Markov state-space modeling for joint distance sensing and data detection in microfluidic molecular communication channels.

Contribution

It develops a novel distance-parameterized Markov state-space model and a low-complexity receiver for joint sensing and communication in molecular channels.

Findings

Accurate distance sensing achieved with the proposed model.

Improved BER performance demonstrated through numerical results.

Mutual benefits observed between sensing and communication tasks.

Abstract

This paper develops a molecular integrated sensing and communication (ISAC) framework that exploits the same molecular observations for physical-parameter sensing and data detection. As a representative instantiation, we consider a microfluidic molecular communication (MC) channel and study transmitter--receiver (TX--RX) distance sensing, where the distance affects the propagation delay, transient response, and inter-symbol interference structure. A distance-parameterized Markov state--space model is established to obtain distance-dependent channel impulse responses and a block observation model for on-off keying signaling. Based on this model, we design a pilot-assisted low-complexity receiver that combines distance initialization, decision-feedback equalization (DFE), and iterative joint refinement. Numerical results show accurate distance sensing and improved bit error ratio (BER), demonstrating the mutual benefit between sensing and communication and highlighting microfluidic MC as a representative platform for molecular ISAC.