An Experimental Demonstration of 2D-Multiple-Input-Multiple-Output-Based Deep Learning for Optical Camera Communication

Abstract

Currently, radio frequency (RF) waveforms are widely used in wireless communication systems and are widely used in many fields to improve human quality of life. In Internet of Things (IoT) systems and satellite systems, the installation and deployment of wireless communication systems have become easier and offer many advantages compared to wired communication. However, high RF frequencies can have detrimental effects on the human body. Therefore, the visible light bandwidth is being researched and used as a replacement for RF in certain wireless communication systems. Several strategies have been explored: free-space optics, light fidelity, visible light communication, and optical camera communication. By leveraging time-domain on–off keying, this article presents a multiple-input-multiple-output (MIMO) modulation technique using a light-emitting diode (LED) array designed for IoT applications. The proposed scheme is versatile and suitable for both roller shutter and global shutter cameras commonly found on the market, including CCTV cameras commonly found in factories and buildings. By using deep learning for threshold prediction, the proposed scheme could achieve better performance compared to the traditional scheme. Despite the compact size of the LED array, the precise control of the exposure time, camera focal length, and channel encoding enabled the successful implementation of this scheme and supported four links at various positions within a communication distance of 22 m, taking into account the mobility effect (3 m/s).