Vessel Network Topology in Molecular Communication: Insights From Experiments and Theory

The concept of synthetic molecular communication (MC) refers to the transmission of information via signaling molecules and is expected 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 only in individual blood vessels but also in complex vessel networks (VNs), as found throughout 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 adapt this model specifically 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 using a dedicated SPION testbed. Validation covers a range of channel topologies, from single-vessel topologies to branched VNs with multiple paths between the transmitter (TX) and the RX. Additionally, to quantify how the VN topology affects signal quality—and inspired by multipath propagation models in conventional wireless communications—we introduce two metrics: molecule delay and multipath spread. We demonstrate that these metrics link the VN structure to molecule dispersion induced by the VN and, indirectly, to the resulting signal-to-noise ratio (SNR) at the RX. The proposed link between VN structure and SNR is validated experimentally, indicating that the proposed framework may be able to guide future 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.

DOI 10.1109/tmbmc.2026.3719315

OpenAlex ID W4417009929