Persistence Length as a Metric for Modeling and Simulation of Nanoscale Communication Networks

This paper explores the significance of persistence length in modeling and simulation of nanoscale networks. Persistence length, used by chemists and biologists, pertains to a wide range of high-aspect ratio, small-scale materials, and has a significant impact on communication at the nanoscale. For example, it applies to carbon nanotubes, microtubules, DNA, and nanowires as well as many other potential nanoscale network structures. Consider the similarities between microtubules in living cells and carbon nanotubes (CNTs). Both microtubules and carbon nanotubes have similar geometric structures and share similar properties; both are capable of transporting information at the nanoscale. Microtubules and carbon nanotubes can also self-organize to create random graph structures, which can be used as communication networks. At the same time, networks of CNTs may be used for molecular-level transport in the human body supporting treatment of diseases. This paper examines fundamental properties of network topologies created by filamentous structures and their relationship to the performance of nanoscale communication networks. This behavior depends strongly on the alignment of bond segments and filaments, which in turn depends on the persistence length of the tubes. We use graph spectral analysis for analyzing a simulated generic network of filamentous structures. A network graph is extracted from the layout of such a structure and graph properties of the resultant graph are examined. The paper presents the results of the simulation with tubes of different persistence lengths and makes the case for including persistence length as a parameter in standards for nanoscale communications. Our simulation results show that at different persistence lengths the network structure and the resultant properties of the graph structure that impact nanoscale communications can be predicted and analyzed. Such standard parameters help in calibrating an analytical model to a physical network allowing for synchronization between analytical and experimental results.