## 6 Performance Evaluation  
This section presents the simulation results on the OCC performance using different values of LED and the camera parameters. Some parameters were kept unchanged, as in Table 2, throughout the simulations. It is worth noting that any variation in the luminaire characteristics will change the simulation results. All the simulations were performed in MATLAB. As discussed in Section 3.3, the maximum number of detectable bits depends on the modulation frequency and shutter speed of the camera. Figure 8 shows how the number of detectable bits varies with camera exposure time for different LED modulation frequencies. The simulation was performed assuming a communication distance of 2 m between the camera and the LED. The exposure time should be controlled to avoid unexpected motion blurs that lead to increased BERs. A high exposure time also contributes to the increase in the projection of noise elements onto the image sensor that affects the overall SINR. Subsequently, variations in SINR affects the BER performance. Recalling Equation (6), it can be seen that the image distortion factor (\(\chi\)) has an immediate impact on the SINR. The SINR variations for OCC with increasing \(\mathrm{d}_{\alpha,\chi}\) for different values of \(\chi\) are illustrated in Figure 9. The effective \(\mathrm{f}_{\alpha}\), \(\mathrm{d}_{\alpha\mathrm{ov}}\), and \(\mathrm{r}_{1}\) were considered as 26 mm, 60\({}^{\circ}\), and 5 cm, respectively. The distance \(\mathrm{d}_{\alpha,\chi}\) was set at 2 m and was kept throughout it. At \(\mathrm{d}_{\alpha,\chi}=0\), the LED appears in the center of the image sensor. With increasing \(\mathrm{d}_{\alpha,\chi}\), at a certain point, the projected image of the LED occupies an area corresponding to the minimum number of strips. Note that a significant drop in SINR can be seen in Figure 9. This is because only a part of the LED appears inside the image sensor as the horizontal distance is increased. The OCC achieves an excellent SINR when the full LED appears inside the image sensor. It can be noted that the SINR decreases with higher values of \(\chi\). For \(\mathrm{d}_{\alpha,\chi}=1\) m, an SINR of approximately 18 dB is reduced when \(\chi\) is increased from 0.1 to 0.4. Hence, lower values of \(\chi\) show significant SINR improvement. The SINR performance can also be improved using cameras with high pixel densities. This is because the spatial separation of the interfering element from the image sensor is facilitated with high-resolution cameras. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**LED Parameters**} \\ \hline Gain of the optical filter, \(\mathrm{g}_{\mathrm{op}}\) & 1 \\ Transmitted optical power & 10 W \\ Half-intensity radiation angle, \(\mathrm{\xi}_{\frac{1}{2}}\) & 60\({}^{\circ}\) \\ \hline \multicolumn{3}{c}{**Camera Parameters**} \\ \hline Image sensor size, \(\mathrm{p}\times\mathrm{q}\) & 6 \(\times\) 4 (3:2 aspect ratio) \\ Pixel edge length, \(\mathrm{\rho}\) & 1.8 \(\mathrm{\SIUnitSymbolMicro m}\) \\ Camera optical to electrical conversion efficiency, \(\mathrm{\xi}\) & 0.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The system parameters for the simulations. Figure 8: The variation of the maximum number of detectable bits with respect to the exposure time. The LED size has a profound impact on OCC performance. A large LED will occupy a high amount of pixels inside the image sensor resulting in a large number of strips. Consequently, the maximum communication distance will be comparatively high. The size of the strips depends on the read-out time and the LED modulation frequency. As discussed in Section 3, the minimum number of strips below which the camera cannot extract the information of the projected image depends on the LED size and the focal length of the camera. Figure 10 shows the number of pixels occupied by the projected image inside the image sensor at different distances from the LED to the camera with different LED sizes and a constant effective focal length of 26 mm.