# Deep Learning-Based Optical Camera Communication with a 2D MIMO-OOK Scheme for IoT Networks
## 4 Implementation Results
### 2D MIMO-OOK Technology  
Figure 4 illustrates the frame structure of the 2D MIMO-OOK system. The four outermost corners of the LED matrix serve as anchor points for corner recognition. Using their coordinates, the positions of all LEDs in the array can be determined through perspective transformation. In this system, an 8 \(\times\) 8 LED array is applied, with 40 LEDs allocated for information communication. As illustrated in Figure 4, the four corner LEDs act as anchors, with anchor 3 specifically enhancing rotational robustness at the receiver. Anchor 4 was the special anchor for the support rotation effect. With anchors 1-3, we used only a single LED with ON status, and three LEDs with OFF status. However, with anchor 4, we used four LEDs with ON status. The special corner is for the support rotation effect. Moreover, 16 LEDs at the anchor positions function as training signals, enabling the camera to distinguish between the ON and OFF states. Each frame includes a preamble to help the receiver identify the frame's starting point. To accommodate variations in frame rate, the SN is incorporated in each packet. This SN helps synchronize the packet transmission rate with the camera's frame capture rate, and its length is adjusted accordingly based on this relationship. After identifying the objects, the OCC signal was designated after the LED region using down-sampling. The 2D MIMO-OOK data was then distinguished by extracting the central intensity of each LED by YOLOv11 [31; 32; 33; 34]. A dataset of 10,000 samples, including both preamble and payload segments, was collected using global shutter and rolling shutter cameras at varying distances (ranging from 1 to 20 m) and speeds. To prevent overfitting, we applied a simple deep learning neural network as revealed in Figure 5. The accuracy of this model achieved more than 95%. Once the preamble was detected, we were able to accurately decode the 2D-MIMO signals, enhancing the performance of the OCC system in mobile scenarios compared to traditional methods. It was observed that using six or more hidden layers could lead to overfitting, reducing the accuracy on test data. In addition, we applied a deep learning model for decoding data. By applying DL for detecting the preamble position and threshold value between bits \"0\" and \"1\" as shown in Figure 5, we improved OCC performance. For deep learning based on the decoder, we used a basic deep learning neural network model with two hidden layers to avoid overfitting the model. To reduce the interference between two LEDs in the LED array, the distance between two LEDs and the communication distance was considered. Due to the camera process based on the pixel in the image, to clarify multiple LEDs in the array, the distance between each LED needed to be higher than one pixel in the image. Figure 3: SNR measurement considering different distances and exposure times. In this study, we conducted multiple deployments of the proposed scheme using a variety of cameras--including webcams, CCTVs, tablets, and a Point Grey camera--to evaluate the impact of frame rate variations. The asynchronous system involves components such as asynchronous decoding, data merging techniques, and missing data detection. The length of the SN is defined based on the overall integration of the system's operations. Figure 6 displays the quantized intensity profiles of the images across different distances and viewing angles. Figure 7 depicts the experimental setup used to validate the 2D MIMO-OOK system, and Figure 8 presents the results obtained using 20 links. By using a focal length of 35 mm, we achieved 20 links at a 25 m distance with a data rate up to 15 kbps. Focal length refers to the distance between a lens and its focal point, where light rays converge or diverge. It is a fundamental property of lenses that determines both magnification and the angle of view in optical systems, such as cameras and the human eye. To increase the communication distance, we used a higher focal length.