# Deep Learning-Based Optical Camera Communication with a 2D MIMO-OOK Scheme for IoT Networks
## 4 Implementation Results
### 2D MIMO-OOK Technology
The implementation outcomes are provided in Table 1. A deep learning decoder was applied to improve OCC performance. For applications in a mobile environment, YOLOv11 was applied to detect the LED matrix. After receiving data, the camera distinguished the preamble and threshold prediction based on the deep learning model. In this study, we proposed deep learning for a 2D MIMO-OOK for long-range and mobile situations. Figure 4: The spatial frame format in an 8 \(\times\) 8 LED array. Figure 5: Deep learning decoder for threshold prediction and preamble detection. Figure 8: Rx interface with 10 links. Figure 6: Quantized intensity profiles of an 8 \(\times\) 8 LED array with different exposure times and distances: (**a**) 1 m and 200 \(\upmu\)s; (**b**) 2 m and 400 \(\upmu\)s.  
Figure 7: System setup with single LED matrix. Figure 6 shows how communication distance and exposure time affect the 8 \(\times\) 8 LED array intensity in captured images, as well as corresponding SNR values. The colors in Figure 6 represent the intensity levels in the image. High intensity is shown in red color, while low intensity is shown in violet color. Although extending the exposure time can increase the effective communication range, it must be carefully considered. As discussed, the camera behaves similarly to a low-pass filter--meaning that longer exposure times reduce the system's bandwidth. This occurs because increased exposure time raises the background noise power, potentially degrading overall signal quality. Figure 9 presents the implementation results using a Point Grey camera across various distances with/without deep learning. The demonstration showed that a deep learning decoder plays a critical role in the performance of the proposed schemes. Even under identical communication distances and noise conditions, the bit error rate (BER) with deep learning had a good performance compared to the non-deep learning decoder. To reduce the BER or extend the communication range, channel coding techniques should be applied to enhance system reliability. Main system parameters are summarized in Table 1. As show in Figure 9, we can see that we can achieve a BER of \(10^{-3}\) with a deep learning model considering a processing time of 0.025 s and achieve a BER of \(10^{-2}\) without a deep learning model at distance of 8 m considering a processing time of 0.02 s. The deep learning model is, therefore, a good candidate to improve OCC performance. In the future, intelligent multi-modal sensing-communication integration [35; 36] will be a feasible path that can facilitate the design of the OCC system and 6G and beyond 6G systems. By applying communication integrated with a sensing model, we may improve the performance of a next-generation communication system. The implementation, which employs a 16 \(\times\) 16 LED matrix with 20 links and 8 \(\times\) 8 LED matrix with 10 links considering a mobile environment, successfully achieves communication at a distance of 20 m as demonstrated in the Supplementary Materials. \begin{table}
\begin{tabular}{c c c} \hline \multicolumn{3}{c}{**Transmitter**} \\ \hline LED types & 8 \(\times\) 8 & 16 \(\times\) 16 \\ \hline FEC & [PERSON] (15,11) \\ \hline \multicolumn{3}{c}{**Receiver**} \\ \hline Camera & PointGrey camera \\ \hline Frame Rate & 60 fps \\ \hline \multicolumn{3}{c}{**Data Rate (kbps)**} \\ \hline Uncode bit rate & 3.840 & 15.360 \\ \hline Code bit rate & 2.816 & 11.264 \\ \hline \end{tabular}
\end{table} 
Table 1: Characteristic parameters of 2D MIMO-OOK scheme in lab environment.