Design and Implementation of a 2D MIMO OCC System Based on Deep Learning

Optical camera communication (OCC) is one of the most promising optical wireless technology communication systems. This technology has a number of benefits compared to radio frequency, including unlimited spectrum, no congestion due to high usage, and low operating costs. OCC operates in order to transmit an optical signal from a light-emitting diode (LED) and receive the signal with a camera. However, identifying, detecting, and extracting data in a complex area with very high mobility is the main challenge in operating the OCC. In this paper, we design and implement a real-time OCC system that can communicate in high mobility conditions, based on You Only Look Once version 8 (YOLOv8). We utilized an LED array that can be identified accurately and has an enhanced data transmission rate due to a greater number of source lights. Our system is validated in a highly mobile environment with camera movement speeds of up to 10 m/s at 2 m, achieving a bit error rate of 10-2. In addition, this system achieves high accuracy of the LED detection algorithm with mAP0.5 and mAP0.5:0.95 values of 0.995 and 0.8604, respectively. The proposed method has been tested in real time and achieves processing speeds up to 1.25 ms.

Automated summary

Optical camera communication (OCC) is a promising wireless technology system that has advantages over radio frequency, such as unlimited spectrum, no congestion, and low operating costs. The main challenge of OCC is identifying, detecting, and extracting data in complex and highly mobile areas. In this paper, a real-time OCC system based on YOLOv8 is designed and implemented to address this challenge. The system achieves a high bit error rate and accurate LED detection algorithm, with processing speeds up to 1.25 ms.