Generating Light-based Fingerprints for Indoor Localization

Radio-frequency solutions (e.g., Wi-Fi, RFID, UWB) are widely adopted but remain vulnerable to multipath fading, interference, and uncontrollable coverage variation. We explore an orthogonal modality--visible light communication (VLC)--and demonstrate that the spectral signatures captured by a low-cost AS7341 sensor can serve as robust location fingerprints. We introduce a two-stage framework that (i) trains a multi-layer perceptron (MLP) on real spectral measurements and (ii) enlarges the training corpus with synthetic samples produced by T abGAN. The augmented dataset reduces the mean localization error from 62.9 cm to 49.3 cm--a 20% improvement--while requiring only 5% additional data-collection effort. Experimental results obtained on 42 reference points in a U-shaped laboratory confirm that GAN-based augmentation mitigates data-scarcity issues and enhances generalization.