# Simulation of an Indoor Visible Light Communication System Using Optisystem
## 6 Results and Discussion
Figure 12:8  
Figure 12:9  
**Direct Path**:  
\(\phi_{r}=22^{\circ}\);  
\(\psi_{r}=25^{\circ}\);  
\(m_{l}=0.64601\);  
\(d=11\) m;  
\((\cos 22)^{0.64601}=0.9523\);  
\((\cos 25)=0.90630\);  
Figure 13:0  
Figure 13:1  
Figure 13:2  
**Mean excess delay**:  
Figure 13:3  
Figure 13:4  
Figure 13:5  
Figure 13:6  
**RMS delay spread**:  
Figure 13:7  
Figure 13:8  
**Bitrate:**  
Figure 13:9  
Similarly, we changed the FOV value from \(30^{\circ}\) to \(90^{\circ}\) and estimated the bitrate. Table 3 summarises the FOV and bitrate results. \begin{table}
\begin{tabular}{c c} \hline
**FOV (Degree)** & **Bitrate (bits/s)** \\ \hline \(30^{\circ}\) & \(2.1208\times 10^{7}\) \\ \hline \(40^{\circ}\) & \(2.7173\times 10^{7}\) \\ \hline \(50^{\circ}\) & \(3.2334\times 10^{7}\) \\ \hline \(60^{\circ}\) & \(3.6526\times 10^{7}\) \\ \hline \(70^{\circ}\) & \(3.9616\times 10^{7}\) \\ \hline \(80^{\circ}\) & \(4.1509\times 10^{7}\) \\ \hline \(90^{\circ}\) & \(4.2147\times 10^{7}\) \\ \hline \end{tabular}
\end{table} 
Table 3: Effect of FOV on the bitrate. Table 3 results indicate that the FOV plays a significant role in bitrate estimation. When we increase the FOV angle from a lower value to a higher value, the bitrate also rises at a specific range. For higher values of FOV, the system needs high-speed switching electronics and high-speed LEDs and phototransistors, increasing the system's cost. Figure 21 presents the relationship between the FOV and bitrate estimation. The results indicate that the FOV value significantly impacts bitrate estimation. Optimizing a FOV angle for a real-time VLC system is necessary for better performance. Our analysis gives a general idea of the FOV effect on bitrate estimation, which is valuable information for VLC system implementation. The hardware design of the VLC transmitter and receiver is tested for further validation. Our experiment is carried out based on the Figures 8-10. We tested our VLC system by considering ten experiments in real time. We transmitted random alphabetic letters from the transmitter and analyzed the received data. We estimated the data recovery rate from the experiments by considering the number of correct alphabetic letters received in LOS and NLOS propagation models. The data recovery rate \((D_{rr})\) is defined as:  
\[D_{rr}(\%)=\frac{N_{drc}}{N_{dt}}\times 100 \tag{37}\]  
where \(N_{drc}\) is the number of data received correctly in the receiver and \(N_{dt}\) is the total number of data transmitted. Figure 22 shows our VLC hardware experiment results in LOS and NLOS conditions. Figure 21: FOV effect on bitrate estimation. From Figure 22, the LOS propagation model-based approach reached an average of 70% data recovery rate. It can be seen from the results that the recovery rate is higher (80%) if the transmitter sends the same alphabet together (Experiment 5). If the transmitter sends unique alphabets, the system has a lower data recovery rate (Experiment 2). In the NLOS propagation model, the system achieved an average of 40% of data recovery. These data recovery results from our experiments indicate that the hardware design of our VLC is a suitable choice for real-time implementation. From all our experiments and results analysis, we demonstrated the relevance of the VLC system, and our results apply to various applications such as communication, positioning, and security [45].