An LED Array-Based 2D MIMO OCC System with Deep Learning for Mobile Environments

Optical wireless communication (OWC) has emerged as a complementary technology to conventional radio frequency (RF)-based communication systems, particularly in scenarios requiring low electromagnetic interference, enhanced security, and efficient spectrum utilization. Within various OWC approaches, optical camera communication (OCC) has attracted increasing attention due to its ability to utilize commercially available image sensors as receivers. This paper presents a 2D multiple-input–multiple-output (MIMO) OCC system based on light-emitting diode (LED) arrays for reliable communication in mobile environments. The proposed system employs on–off keying (OOK) modulation, which supports both rolling shutter and global shutter cameras. To improve decoding reliability under mobility conditions, a deep learning-based decoding model is introduced to enhance LED state detection compared with conventional zero-crossing approaches. In addition, a sequence number-based synchronization is implemented to compensate for frame rate variation and packet missing in a real-time environment. Besides that, by applying YOLOv13 for light source detection and tracking, we can achieve 98% accuracy at 3 m/s velocity. Experimental results show reliable communication performance at transmission distances of up to 22 m under various mobility conditions. Furthermore, the proposed system is validated through real-time environmental data transmission using temperature and humidity sensors with 20 links. The results indicate that the proposed scheme provides stable and reliable OCC performance for mobility Internet of Things (IoT) applications.