Vessel Network Topology in Molecular Communication: Insights from Experiments and Theory
By: Timo Jakumeit , Lukas Brand , Jens Kirchner and more
Potential Business Impact:
Lets tiny particles send messages inside your body.
The notion of synthetic molecular communication (MC) refers to the transmission of information via signaling molecules and is foreseen to enable innovative medical applications in the human cardiovascular system (CVS). Crucially, the design of such applications requires accurate and experimentally validated channel models that characterize the propagation of signaling molecules, not just in individual blood vessels, but in complex vessel networks (VNs), as prevalent in the CVS. However, experimentally validated models for MC in VNs remain scarce. To address this gap, we propose a novel channel model for MC in complex VN topologies, which captures molecular transport via advection, molecular and turbulent diffusion, as well as adsorption and desorption at the vessel walls. We specialize this model for superparamagnetic iron-oxide nanoparticles (SPIONs) as signaling molecules by introducing a new receiver (RX) model for planar coil inductive sensors, enabling end-to-end experimental validation with a dedicated SPION testbed. Validation covers a range of channel topologies, from single-vessel topologies to branched VNs with multiple paths between transmitter (TX) and RX. Additionally, to quantify how the VN topology impacts signal quality, and inspired by multi-path propagation models in conventional wireless communications, we introduce two metrics, namely molecule delay and multi-path spread. We show that these metrics link the VN structure to molecule dispersion induced by the VN and mediately to the resulting signal-to-noise ratio (SNR) at the RX. The proposed VN structure-SNR link is validated experimentally, demonstrating that the proposed framework can support tasks such as optimal sensor placement in the CVS or the identification of suitable testbed topologies for specific SNR requirements. All experimental data are openly available on Zenodo.
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