## 1 Introduction  
Optical wireless communication (OWC) has recently become a congruent complement of radio-frequency (RF) technologies due to its vast unregulated spectrum, as well as the dense formation of lighting infrastructures in both indoor and outdoor scenarios [1]. It has introduced a new venture for the Internet of Things (IoT) by increasing connectivity options. The optical spectrum is entirely cost-free and is a significant option to assist RF in handling the massive data traffic envisaged for the future. Optical camera communication (OCC) is a subsystem of OWC that uses visible or infrared light to communicate with a camera sensor [1]. Complementary metal-oxide-semiconductor (CMOS) cameras integrated into smartphones or mobile robots have become very common in recent years, making OCC immensely promising. Current commercial light-emitting diodes (LEDs) offer low costs, a low power consumption, high efficiency, and are established almost everywhere. Exploiting cameras to receive data sent from LED transmitters adds a significant dimension in the area of OWC systems. Accordingly, IEEE has developed a task group regarding OCC, called IEEE 802.15.7m [2]. OCC is a directional technology that provides the users with a high level of security. Moreover, due to the nature of camera pixels, an OCC system is less affected by interference than any other OWC technologies. These characteristics open up possibilities for the system's usability in a variety of applications, including vehicle-to-vehicle and vehicle-to-infrastructure (V2X) communications [3; 4] digital signage, augmented reality (AR), and location-based services (LBS) [5; 6; 7]. However, several challenges have been observed while communicating using image sensors. First, cameras cannot detect LEDs blinking at a high frequency [8]. Second, very low frequencies lead to flickering observed by the human eyes, which is prohibited. Third, conventional cameras offer low sampling rates, resulting in a low-rate data reception. Fourth, synchronization between the LED modulation frequency and the camera shutter speed is also an important challenge for OCC. Without proper synchronization, the bit-error rate (BER) increases. Although OCC is very stable in terms of changing the communication distance, the limited angle-of-view (AOV) leads to the system not exploiting the full LED coverage area. Moreover, an image should appear in several pixels to decode the information sent from the LED. Therefore, the fifth reason that significantly affects OCC performance accounts to the pixel edge length. Different methods can be employed to improve OCC performance. Deep-learning-based algorithms can be utilized to increase the data rate and communication distance. A convolutional neural network was proposed in [2] in conjunction with OCC to receive data in outdoor environments. Communication in these scenarios is affected by sunlight and bad weather conditions (fog, rain, or snow). Computer vision techniques can also be utilized for accurate LED pattern classification and recognition to increase the signal-to-interference-plus-noise ratio (SINR), communication distance, and data rate for OCC. In recent times, red/green/blue (RGB) LEDs are used in OCC to improve the performance. Deep learning is a significant option to design the transceiver for RGB-LED-based communications [9]; however, this technique faces some limitations, such as its computation expensiveness and configuration complexities. Researchers have been investigating how to enhance the OCC performance for years. Enhancement of the signal performance of mobile-phone-based OCC using histogram equalization was reported in Reference [10]. An improved decoding method for OCC for vehicular applications using high-speed cameras was presented in Reference [11]. Recently, researchers have been exploring how to increase the maximum communication distance and data rate by introducing new modulation schemes. In Reference [12], a combination of modulation schemes has been proposed to achieve an improved data rate.