# Deep Learning-Based Optical Camera Communication with a 2D MIMO-OOK Scheme for IoT Networks
## 1 Introduction  
The IoT system is now integrated into numerous aspects of human life, such as smart homes, e-health, and smart cities [1]. IoT systems enable devices to connect seamlessly to the internet using communication protocols designed for data transmission [2]. These systems encompass physical devices, microcontroller units (MCUs), animals, and even humans, facilitating automatic communication and network connectivity without direct human intervention. With the growth of Industry 4.0, the IoT plays a crucial role in enabling rapid interaction between people and their surroundings. However, most IoT technologies, including LoRa, ZigBee, Bluetooth, and Wi-Fi rely on RF systems, which raises concerns about potential health risks [3]. It is most critical in special environments, such as schools, hospitals, and nursing homes, where weak populations--including children, elderly individuals, and patients--are present. Because of these health issues, many researchers are actively looking for alternative technologies to replace RF-based communication in applications. One promising alternative is use of the light spectrum for information transmission, with three leading technologies emerging: VLC, Li-Fi, and OCC. Although VLC/Li-Fi rely on photodiodes (PDs) to detect LED light intensity, OCC deploys image sensors as receivers. Among the available camera types in the market, global shutter cameras and rolling shutter cameras are broadly used for OCC systems [4]. Compared to RF technologies, OWC offers some advantages:  
* Safety: Unlike RF waves, visible light waves do not pose health risks to humans [5]. The RF waveform, however, can have harmful effects and may also cause system performance issues due to electromagnetic interference. * Higher bandwidth: the bandwidth of the light waveform is over 1000 times greater than that of RF, making it more efficient for data transmission. * Secure and efficient transmission: visible light waves offer high safety and more efficient communication. Recognizing the potential of OWC, many companies have invested significant resources in research and development to enhance this technology. The IEEE 802.15.7-2011 standard [6] presented OWC, considering a simple method in 2011, primarily focusing on Visible Light Communication (VLC). After that, the IEEE 802.15.7-2018 standard [7] was proposed as an updated version, incorporating additional advancements in OWC. The IEEE 802.15.7a-2024 standard follows enhancements to the physical (PHY) layer [4]. Due to advancements in manufacturing technology, LEDs offer several benefits, such as a long lifespan, low power consumption, and availability in various sizes and operational modes compared to conventional light. They are, therefore, one candidate for a future lighting system instead of normal light in the market. Moreover, one key feature that makes LEDs ideal for OWC systems is their ability to switch on and off speedily [8], allowing high-speed data communication. At present, RF systems dominate various applications, including communication networks, monitoring systems, and radar technology. Nevertheless, RF signals produce electromagnetic interference (EMI) [9], which has been connected to possible health hazards, especially with regard to brain function [10]. In contrast, OWC technologies are emerging as a good, EMI-free substitute to RF-based communication systems [11]. In VLC and Li-Fi systems, PDs detect LED intensity by interpreting the rapid switching of LEDs between on and off states [12]. Research efforts [13; 14] have demonstrated ultra-high-speed pulse density modulation with highly efficient spectral properties using photodiodes. Additionally, studies [15; 16] have implemented MIMO techniques to enhance the data rate through multi-channel VLC/Li-Fi systems. However, VLC and Li-Fi systems face limitations due to their reliance on photodiodes as detectors. The communication range is quite short, making it more suitable for indoor applications.