## 3 Experiment Results and Analysis
Figure 9 shows the original captured image frames with grouping grid and diffuser, quantized intensity of the detected data, and the top view of intensity profiles for \(L\) of 20, 60, and 100 cm, and SS of 200, 600, and 800 \(\upmu\)s. The received intensity distribution within the image frame is shown in the form of quantized intensity profiles of the captured original images. These intensity profiles play an important role in determining the higher and lower intensities representing 1 and 0 bits in the received image frames for further thresholding and demodulation [39]. The dotted yellow box in the original image frames is the ROI, which fills only the captured Tx within the full image frame. The clear and sharp distinction between data lines of the adjacent \(N_{\text{groups}}\) can be seen at SS of 200 \(\upmu\)s (see Figure 9a), while the lines get saturated for higher values of SS (see Figure 9b,c), which affects data demodulation (i.e., a higher number of bit error). Note that the camera's SS can be used to combat the effect of ambient lights, where lower and higher SS results in lower and higher levels of light to pass through the camera lens, as seen in the captured images in Figure 9.  
Based on the received bits in the image frames, the data throughput is given as:  
\[\text{Data throughput}=N_{\text{groups}}\times N_{\text{visible}}(\frac{ \text{fps}}{2}), \tag{8}\]  
Figure 10 illustrates the data throughput and \(N_{\text{visible}}\) with respect to \(L\). Note that \(N_{\text{visible}}\) is the number of visible bits in each group. The maximum data throughput of 960 bps is observed at the minimum distance of 20 cm, where 8 full bits are visible in each group. The Tx illumination surface reduces with the increased \(L\), thus resulting in reduced \(N_{\text{visible}}\) and the data throughput. For \(L\) of 100 cm, only one full bit is visible, therefore the data throughput is reduced to 120 bps. Figure 8: MIMO-OCC performance analysis without grouping grid and diffuser: captured images for data detection and their grayscale intensity profiles for: (**a**) GS captured image for \(L\) = 20 cm; (**b**) RS captured images for \(L\) = 5 cm; and (**c**) RS captured images for \(L\) = 7. Table 3 shows the predicted data throughput based on the approximation of different Tx configurations, \(L\), \(N_{\text{groups}}\), and \(N_{\text{visible}}\). It can be seen that increasing the number of LEDs leads to a larger surface area of the Tx, thus limiting higher values of \(N_{\text{visible}}\). For example, for a Tx using a \(24\times 24\) LED array, \(24\)\(N_{\text{groups}}\) can be formed, which will increase the data throughput by up to 7.92 and 0.360 kbps for \(L\) of 60 and 160 cm, respectively; while for a Tx with a \(32\times 32\) LED array, \(32\)\(N_{\text{groups}}\) can be formed, which will increase the data throughput by up to 13.44 and 0.480 kbps for \(L\) of 80 and 180 cm, respectively. Figure 10: MIMO-OCC performance analysis: the data throughput with respect to \(N_{\text{visible}}\) bits. Figure 9: MIMO-OCC performance analysis with grouping grid and diffuser: the quantized intensity profiles of originally captured images for data detection at: (**a**) \(L=20\) cm and SS = 200 \(\upmu\)s; (**b**) \(L=60\) cm and SS = 600 \(\upmu\)s; and (**c**) \(L=100\) cm and SS = 800 \(\upmu\)s.  
Since in OCC the data is captured in the form of a two-dimensional image, a conventional signal-to-noise ratio (SNR) measurement cannot fully reflect the quality of the link. Therefore, we have adopted peak signal-to-noise ratio (PSNR), which is widely used as a quality metric in image processing systems.