# Design and Implementation of a 2D MIMO OCC System Based on Deep Learning
## 2 Literature Review  
In recent years, extensive research has been conducted to achieve superior performance in OCC systems, allowing for their application in a variety of disciplines. In their study, [PERSON]. [20] introduced an implementation of the OCC technique which utilizes an indoor positioning system and employs On-Off Keying (OOK) modulation. However, the effective range of communication was limited to a mere 2.7 m, with both the receiving and transmitting devices remaining stationary. The OCC system was developed and deployed using LabView, as documented by [21]. However, the utilization of LabView in compact, cost-effective modules such as the Jetson Nano Kit poses certain implementation difficulties.  
Another study [22] investigated the phenomenon of blurring in optical vehicle communication systems, including motion blur and blurring induced by rainy, foggy, or snowy weather conditions, in an effort to improve OCC performance in a high-mobility environment. The presence of other light sources has the potential to induce a rise in the bit error rate (BER) of the system as a result of the interference they generate at the receiver. The author proposed a neural network (NN)-based method for performing RoI selection and detecting the target transmitter on the receiver of a VLC or OCC system to mitigate the impact of parasitic light incidents. The author of a research study [23] proposed the utilization of OCC-based convolutional neural networks (CNNs) for the purpose of detecting LEDs within the context of the Internet of Vehicles (IoV). Nevertheless, the implementation of OCC systems developed in Matlab is a significant challenge when applied to devices that have limited computational resources. Furthermore, the precision of the detected LEDs is only 60%.  
In order to tackle the issue of accuracy and real-time implementation, the researchers utilized the YOLOv2 model for the purpose of identifying RoI. This model has been proven to reach the highest level of accuracy while maintaining real-time performance, as stated in the study [24]. The proposed methodology effectively produces ROI in systems, demonstrating a significantly high processing frame rate and satisfactory detection accuracy. However, the experimental findings pertaining to LED recognition indicate that the transmission distance is limited to a mere range of 0.5 to 1 m.  
[PERSON] et al. [25] proposed a MIMO C-OOK scheme based on rolling-shutter effect for the mobility environment using a deep learning network. This method achieved a communication distance of up to 22 m with a considerably low bit error rate when considering the movement speeds at 2 m/s. However, the proposed method only works on the rolling-shutter camera which limits its implementation.  
[PERSON] et al. [26] proposed the utilization of CNN within the realm of VLC to enhance the precision of classifying Optical Fringe Codes (OFC) to a level surpassing 95%. Nonetheless, this approach necessitates that the RGB-LED and camera be aligned in parallel. Further advancements in VLC and OCC technologies are needed to enhance the transmission range and facilitate LED identification at different angles and distances in IoT applications.  
Although significant progress has been made, experimental demonstrations of the efficacy of an OCC system in high-mobility environments are limited. In addition, there is a dearth of research into lightweight deep-learning models for OCC that can improve mobility, communication distance, and resilience against optical noise sources. Consequently, the objective of this study is to present an OCC system that incorporates YOLOv8 for LED detection and tracking on the receiver side. YOLOv8 [27] is designed to be a lightweight, quick, accurate, and precise model, making it the ideal option for detecting and tracking LEDs in the OCC system. By integrating the YOLOv8 model into the OCC system, our suggested approach offers enhanced mobility, precise detection capabilities, and real-time performance on resource-constrained devices.