# Design and Implementation of a 2D MIMO OCC System Based on Deep Learning
## 1 Introduction  
The rapid progression of technology has led to the development of innovative technologies that have wide-ranging implications for human existence. The Internet of Things (IoT) is currently regarded as one of the most widely used technologies in recent years [1, 2]. The utilization of this technology facilitates the exchange of data and the implementation of integrated control systems, leading to the generation of massive amounts of information and enhanced operational effectiveness across diverse industries. The significance of radio frequency (RF) in the context of wireless data transmission for IoT technologies cannot be neglected [3]. Furthermore, RF technology provides reliable communication channels that give wide coverage and a substantial data rate. Nevertheless, the utilization of RF spectrum encounters some challenges, such as limited spectrum availability, electromagnetic interference (EMI), multipath reflections that can lead to fading and degrade signal transmission quality, stringent regulatory constraints governing its operation, and vulnerability to unauthorized intrusions [4, 5, 6]. Furthermore, the health sector has raised concerns regarding the potential health effects of prolonged exposure to RF radiation [7, 8, 9, 10]. As a result, optical wireless communication (OWC) has garnered significant attention as a potential solution due to its advantages, including high-speed transmission, high levels of security, low energy consumption, and high resistance to electromagnetic interference [11, 12, 13]. LiFi, OCC, visible light communication (VLC), and free-space optical (FSO) communication are considered to be the most promising OWC technologies. Each of these technologies exhibits a distinctive architectural structure, communication protocol, propagation medium, and application scenario. OCC is a subsystem of OWC that employs a complementary metal-oxide-semiconductor (CMOS) camera as an optical receiver (Rx) to detect signals in visible light (VL) or infrared radiation (IR). In OCC, data are encoded into an invisible frequency-modulated light signal. The CMOS camera on the receiving device captures and decodes the light signal into communication-friendly data. By employing light as the communication medium, OCC offers benefits in terms of data security with minimal risk of cyber-attacks and information leaks [14]. In addition, OCC does not pose a threat to ocular health, as the frequencies of light employed in VLC are generally regarded as safe [15]. The IEEE standard 802.15.7-2018 [16] has been implemented to govern and provide technical guidance for OCC development and interoperability. In recent years, there has been a notable increase in the use of LED lighting infrastructure across various indoor and outdoor environments in response to their manifold advantages, such as their low energy consumption and robustness. The aforementioned progress has made a major contribution to the increasing capabilities of OCC. Furthermore, a significant amount of research has been conducted on the integration of OCC into automobile technologies. The increasing number of vehicles and roadside units (RSU) has led to the recognition that the importance of OCC will grow, owing to its wide range of applications. These applications consist of platooning, electronic emergency brake lighting, intersection assistance, and forward collision warning [14]. Despite the numerous benefits of the OCC system, there are certain problems that necessitate attention in its development, particularly in regions characterized by high mobility as well as elevated levels of noise and interference. The initial issue emerges in scenarios characterized by high mobility, wherein the occurrence of optical flow on the image sensor can lead to a reduction in the accuracy of LED identification. This, in turn, has a simultaneous impact on the performance of communication [17; 18; 19].