Image-based VLC Signal Demodulation Using Machine Learning
This letter presents an image-based demodulation technique for OOK-modulated VLC signals from air quality sensors. We optimized system performance by transforming received signals into images using segmentation algorithms, bicubic interpolation, and image thresholding, enhancing demodulation accuracy through data augmentation. Experimental results show that our ML-driven demodulator achieves 97.58% accuracy, an extended communication range of up to 10 m, and improved noise tolerance. These advancements indicate that our proposed system is more efficient than conventional demodulation schemes. In future studies, we aim to improve VLC range, data rate, and noise tolerance by replacing the photodiode with an image sensor for various modulation schemes. We will also explore the effects of varying light conditions and signal interference in real-world VLC environments.
Nov 22, 2024
