Molecular ISAC via Markov State-Space Modeling: Joint Distance Sensing and Data Detection
For researchers in molecular communication, this work introduces a novel ISAC framework that jointly performs sensing and communication, though it is an incremental step building on existing Markov state-space models.
This paper proposes a molecular integrated sensing and communication (ISAC) framework for microfluidic molecular communication, enabling joint distance sensing and data detection. Numerical results demonstrate accurate distance sensing and improved bit error ratio (BER), showing mutual benefit between sensing and communication.
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.