## 1 Introduction
In VLC and LiFi technologies, photodiodes receive information at an intensity that is based on the ON/OFF status of the light sources in the transmitter [12]. In [13; 14], a photodiode-based ultra-high-speed pulse-density modulation with exceptionally effective spectrum characteristics was proposed. Multiple-input multiple-output (MIMO) is a technology proposed in [15; 16] to enable large data rate transfers using ultra-high-speed multi-channels. As noted, VLC/LiFi/FSO systems employ photodiodes as detectors. These present some disadvantages. For example, they can only be employed for short-distance applications and are extremely sensitive to the mobility effect. Outdoor environments can also cause problems for an LED signal aimed at receiving signals from photodiodes. Using the OCC technique, however, a longer communication distance (up to 200 m) could be achieved [17] because an image sensor is used rather than photodiodes. In [18], [PERSON] et al. considered how image sensor types affected an OCC system. When using a global shutter camera, the frame rate of the camera will affect the data rate of an OCC system according to Nyquist's law. Therefore, when using a rolling-shutter camera, the frame rate and the rolling-shutter speed of the camera should be considered. In [18], the authors noted the effects of the camera's focal length and the exposure time on communication distance. Currently, LiFi technology can be deployed at a 10 m communication distance in an outdoor environment using a photodiode lens (2 inches) [19]. Region of interest (RoI) signaling was proposed and standardized [5] in PHY IV (IEEE 802.15.7-2018), which was deployed to detect multiple LEDs in an OCC system. The document [20] introduced the camera on-off keying (C-OOK) scheme, which can sustain a high data rate. This scheme was also standardized in IEEE 802.15.7-2018. However, the C-OOK scheme has some drawbacks, e.g., a high bit error rate (BER) and a short communication distance [21]. A proposed MIMO C-OOK technique that achieve a long distance with a lower BER value was achieved by using a matched filter. In [22], we proposed a rolling-shutter orthogonal frequency-division multiplexing (OFDM) scheme for a high data-rate OCC system. Depending on the rolling-shutter effect of the camera, the OFDM waveform could be received according to the LED intensity values in each image. The 2D OFDM scheme for a screen-to-camera technique was developed by [PERSON] et al. [23] with a data rate of more than 50 kbps. This scheme has the following disadvantages: large transmitter size, short communication distance, and an expensive transmitter achieving a high data rate. \"Deep learning\" is known as a subset of machine learning techniques based on artificial neural networks that support some notions for resolving OCC problems including accurate object detection, robustness, high data rate, and real-time processing for mobile environments. In [24], version 5 of the You Only Look Once (YOLOv5) algorithm was proposed to optimize the hyperparameters in a real-time process for Underwater Detection. The optical fringe codes (OFC) proposed for OCC, based on a convolution neural network (CNN) with 95% precision, are presented in [25]. However, the mobility effect was not considered. In the present work, we propose an LED detection method based on deep learning using the YOLOv5 algorithm to achieve high accuracy (detection accuracy of more than 97%) in a real-time process for a C-OOK scheme. Additionally, a deep learning decoder was also proposed to increase the OCC performance compared with the conventional decoder [21] method. The remainder of this study comprises four sections. In Section 2, we explain the contributions of the study, i.e., using deep learning to improve the OCC performance. Section 3 illustrates the system architecture of the MIMO C-OOK scheme using deep learning. The study's implementation results are described in Section 4, and the conclusion of this study is discussed on Section 5.