aiqbench analysis
This research proposes a novel signal detection method for molecular communications by utilizing Legendre polynomial expansions to analyze directional signatures on receiver surfaces. By leveraging spatial hit patterns rather than simple molecule counts, the approach offers improved efficiency and performance over traditional detection schemes. The work bridges information theory with advanced mathematical modeling to solve fundamental problems in emerging nanoscale communication networks.
Receiver-Surface Hit Patterns via Legendre Approximation for Molecular Signal Detection
arXiv:2609.12089v1 Announce Type: cross Abstract: Detecting whether a transmitter is actively communicating with a receiver is a fundamental problem in molecular communications. A spherical receiver may measure not only the number and arrival times of absorbed molecules, but also their absorption locations on the receiver surface. These locations contain a directional signature that is lost in count-only detection. In this letter, we develop a molecular signal detector based on a Legendre polynomial expansion of the receiver-surface hit density and extend it to a Legendre-based Viterbi sequence detector. By exploiting the surface-level directional signature of molecular arrivals, the proposed detectors provide geometry-aware, efficient, and better-performing alternatives to count-only detection.
Abstract:Detecting whether a transmitter is actively communicating with a receiver is a fundamental problem in molecular communications. A spherical receiver may measure not only the number and arrival times of absorbed molecules, but also their absorption locations on the receiver surface. These locations contain a directional signature that is lost in count-only detection. In this letter, we develop a molecular signal detector based on a Legendre polynomial expansion of the receiver-surface hit density and extend it to a Legendre-based Viterbi sequence detector. By exploiting the surface-level directional signature of molecular arrivals, the proposed detectors provide geometry-aware, efficient, and better-performing alternatives to count-only detection.
Information Theory (cs.IT); Machine Learning (cs.LG)
arXiv:2609.12089 [cs.IT]
(or arXiv:2609.12089v1 [cs.IT] for this version)
https://doi.org/10.48550/arXiv.2609.12089 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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