Bit Segmentation of Non-Line of Sight Data in Optical Camera Communication Using U-Net

Abstract

Optical Camera Communication (OCC) utilizes image sensors to decode modulated light signals from light-emitting diodes (LEDs), offering a cost-effective solution for wireless communication. However, data extraction in non-line-of-sight (NLOS) conditions is challenging due to signal distortions caused by obstacles and reflections. Traditional segmentation techniques, such as Otsu's thresholding and adaptive thresholding, are computationally efficient but struggle with lighting variations, background interference, and high-frequency distortions, limiting their effectiveness in real-world OCC applications. To address these limitations, we propose a U-Net convolutional neural network, trained on a diverse dataset covering various camera distances, lighting conditions, and reflection levels to improve segmentation accuracy. The proposed model achieves up to 25% BER improvement, outperforming traditional thresholding methods and ensuring more reliable bit extraction in challenging OCC environments. These advancements make deep learning a promising approach for improving OCC applications such as indoor positioning, smart transportation, and secure optical wireless communication.