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

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Summary
This summary is machine-generated.

This study introduces a real-time Optical Camera Communication (OCC) system using YOLOv8 for high mobility scenarios. The system demonstrates reliable data transmission with excellent LED detection accuracy, overcoming key OCC challenges.

Keywords:
LED segmentationYOLOv8object detectionoptical camera communication (OCC)

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Area of Science:

  • Optical Wireless Communication
  • Computer Vision

Background:

  • Optical Camera Communication (OCC) offers advantages like unlimited spectrum and low cost over radio frequency.
  • High mobility presents challenges for reliable data detection and extraction in OCC systems.

Purpose of the Study:

  • To design and implement a real-time OCC system capable of operating effectively in high mobility conditions.
  • To address the limitations of current OCC systems in complex, dynamic environments.

Main Methods:

  • Utilized the You Only Look Once version 8 (YOLOv8) algorithm for real-time object detection.
  • Implemented an LED array for enhanced data transmission and accurate identification.
  • Tested the system in a high mobility environment with camera speeds up to 10 m/s.

Main Results:

  • Achieved a bit error rate of 10-2 in a highly mobile environment (10 m/s at 2 m).
  • Demonstrated high accuracy in LED detection with mAP0.5 of 0.995 and mAP0.5:0.95 of 0.8604.
  • Real-time processing speeds up to 1.25 ms were achieved.

Conclusions:

  • The proposed YOLOv8-based OCC system effectively enables communication in high mobility conditions.
  • The system overcomes significant challenges in real-time data extraction and LED detection for OCC.
  • This advancement paves the way for more robust optical wireless communication solutions.