Year: 2026
Type: article
Abstract: Visible light communication (VLC) can support indoor positioning by exploiting existing lighting infrastructure, but conventional VLC positioning often remains coupled to active illumination. Dark-light VLC (DL-VLC) decouples localization from illumination by using short optical pulses that are detectable by photodetectors while remaining visually imperceptible. This study evaluates a semantic-aware DL-VLC localization framework in which received signal strength (RSS), angle of arrival (AOA), incidence angle, and signal-to-noise ratio (SNR) features are mapped to a compact task-oriented representation before coordinate regression. A simulated \(5\times 5\times 3~\textrm{m}^{3}\) room with four ceiling-mounted transmitters and a photodetector receiver at \(0.85~\textrm{m}\) height was used to generate line-of-sight Lambertian channel features. The model was compared with regressors based on decision trees, random forests, support vector regression, AdaBoost, k-nearest neighbors, stochastic gradient descent, and a direct multilayer perceptron under different training densities and channel degradation settings. With 5000 training points, the semantic-aware model achieved a mean localization error of \(1.740~\textrm{cm}\) and a root-mean-square error (RMSE) of \(2.360~\textrm{cm}\) , compared with \(2.233~\textrm{cm}\) and \(3.120~\textrm{cm}\) for the best-performing classical baseline. These results indicate that, in the adopted simulation model, a semantic-aware representation improves localization accuracy, task success rate, robustness to modeled channel degradation, and normalized energy per successful decision relative to the tested baselines.
Source: Scientific Reports
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Awards: G6979243908, G8558699768
OpenAlex ID: W7203640730
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