# Design and Implementation of a 2D MIMO OCC System Based on Deep Learning
## 6 Conclusions  
In this paper, we propose an LED detection, tracking, and segmentation system based on YOLOv8s in conjunction with the conventional image processing method used in the OCC system. Specifically, we proposed the YOLOv8s model, which is a viable and effective model for LED detection due to its ability to function in real time while preserving detection precision. In addition, our OCC system's combination of object detection and tracking aids in reducing the system's processing time.  
Therefore, our system is capable of real-time operation on embedded devices with limited configurations. Furthermore, we propose a method for LED segmentation recognition based on conventional image processing which reduces processing complexity compared to deep neural network algorithms while maintaining a high level of precision. Additionally, we have implemented a system to evaluate the performance of the OCC system at different transmission ranges in mobile environments. The results demonstrate that our OCC system exhibits high efficacy at a range of 10 m in a low-mobility environment and achieves high mobility (speed = 10 m/s) at a range of 2 m. However, obstacles remain when the system operates with high mobility over long distances. Our design can be utilized in future OCC systems that require a high degree of mobility.  
In the future, further research on OCC can explore the utilization of lasers as the transmitter to increase the transmission distance and investigate novel modulation schemes to enhance the data rate of this system. Additionally, developing a cloud-edge architecture with a lightweight OCC approach on resource-constrained devices should be considered. Finally, there is a need to validate the feasibility of implementing the proposed scheme in real-world scenarios.  
Conceptualization, O.S.S. and V.L.N.; methodology, O.S.S. and V.L.N.; software, O.S.S., V.L.N. and R.F.P.; validation, O.S.S. and R.F.P.; formal analysis, O.S.S. and R.F.P.; investigation, O.S.S. and M.M.F.; resources, O.S.S. and H.N.; data curation, O.S.S. and M.M.F.; writing--original draft preparation, O.S.S., V.L.N. and R.F.P.; writing--review and editing, O.S.S., R.F.P. and H.N.; visualization, O.S.S. and R.F.P.; supervision, Y.M.J.; project administration, Y.M.J.; funding acquisition, Y.M.J. All authors have read and agreed to the published version of the manuscript.  
Figure 11: The BER performance of our scheme at different communication distances with changes in the speed of the camera (**a**) 2 m; (**b**) 5 m; (**c**) 8 m; (**d**) 10 m.  
**Funding:** This research was supported by the Ministry of Science and ICT (MSIT), South Korea, under the Information Technology Research Center (ITRC) support program (IITP-2023-2018-0-01396) supervised by the Institute for Information and Communications Technology Planning and Evaluation (IITP), and a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2022R1A2C1007884).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.