# Deep Learning-Based Optical Camera Communication with a 2D MIMO-OOK Scheme for IoT Networks
## 1 Introduction
In outdoor environments, channel distortions hinder the photodiode's ability to effectively receive LED signals. However, OCC, which utilizes image sensors as receivers, can operate over significantly longer distances up to 150 m [17]. Research [18] has highlighted the impact of different types of cameras on the OCC system. In global shutter cameras, factors such as frame rate in the receiver side and packet rate in the transmitter side must be cautiously managed according to [PERSON]'s theorem. When it comes to rolling shutter cameras, we need to take into account both the rolling shutter speed and the frame rate. It is also important to control several parameters, such as exposure time, camera focal length, and signal-to-noise ratio (SNR), in order to increase the communication range. Currently, Li-Fi technology can achieve a maximum transmission distance of approximately 10 m by incorporating photodiode lenses [19]. Optical Camera Communication (OCC) techniques enable the easy implementation of Multiple-Input Multiple-Output (MIMO) systems, allowing simultaneous communication with multiple LEDs using a single image sensor. Though, achieving MIMO with PDs is challenging, especially when handling a large number of connections. A commonly used algorithm in OCC systems is the Region of Interest (RoI) algorithm, which helps detect multiple objects. Based on the rolling shutter effect, ref. [20] presented a Camera On-OffKeying (C-OOK) to design the high-speed system. However, this approach has limitations, including a limited communication range and reduced performance. In [21; 22], authors proposed RS-OFDM, a scheme that combines Orthogonal Frequency Division Multiplexing (OFDM) with the rolling shutter camera, to enhance data rates in OCC systems. In [23], a color intensity modulation-MIMO technology was introduced, achieving data using a global shutter camera operating at 330 frames per second with a maximum distance of 1.4 m. However, the global shutter camera is expensive and not broadly available. Moreover, applying colors for signal transmission introduces challenges compared to the On-Off Keying (OOK) scheme, such as a limited transmission range and higher bit error rates. To address these issues, the authors in [24; 25; 26] developed a MIMO scheme using an LED array. The Discrete Hartley Transform (DHT) IV algorithm is a mathematical transformation like the Discrete Fourier Transform. It is applied for image and video processing, and computer vision with low latency. While this method mitigates flicker effects, it has several disadvantages, including a short communication range (1.4 m) and a lack of rotational support, which are critical for OCC-based IoT systems. In this study, we suggest a monitoring system utilizing an OCC framework that integrates RoI signaling and MIMO techniques considering an LED array. This OCC scheme is compatible with both rolling shutter cameras and global shutter cameras, and it is particularly suitable for deployment with Closed-Circuit Television (CCTV) cameras, which are widely available. The system's adaptability to existing CCTV infrastructure enhances its applicability in environmental monitoring. By applying deep learning to a 2D MIMO-OOK scheme, we achieve a communication range of up to 20 m with 20 links simultaneously. The remainder of study is presented as follows: Section 2 highlights the contributions of the proposed approach. Section 3 presents the system architecture of deep learning-based Optical Camera Communication. In Section 4, implementation results are shown over a long distance and with a low data rate. Finally, Section 5 concludes this study.