# Experimental Evaluation of Display Field Communication Based on Machine Learning and Modern Design
## 4 Laboratory Experimentation of the Proposed DFC Scheme
### Channel Coding  
To receive and decode the data at the receiver with minimal errors, a channel coding method has been used. Channel coding acts as an error detector when the data are mistakenly received at the receiver or lost. In addition, it acts as an error corrector that restores the original data. In a real-world experiment, noise caused by the optical wireless channel has a significant impact on error performance. Therefore, the role of channel coding becomes very important for creating a system that is more robust and reliable against noise. Among many channel coding techniques, we used turbo coding, which is excellent in error detection and correction [34].  
Figure 3 depicts the error rate comparison of the proposed DFC scheme with and without channel coding. The plot was generated using three different RGB channels of the color Lena image. We can get several insights from the plot. First, we can observe the degradation of BER with increasing PSNR. Although high PSNR means a better image quality, it is achieved at the cost of degraded communication performance. From Equations (7)-(9), we can note that the PSNR of the image has an inverse relationship with \(\alpha\). With a small value of \(\alpha\), the visual quality of the data-embedded image is well preserved, and the PSNR is high. However, it decreases the data power. Therefore, it lowers the probability of successful  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Specification** \\ \hline Transmitter display & Samsung monitor, 51 cm \(\times\) 29 cm, Res. \\  & \(1920\times 1080\) p, Refresh rate 60 Hz \\ Receiver camera & Apple iPhone XS Max, 12 MP (\(1920\times 1080\)), \\  & Frame rate 60 fps, Field of view 59.67\({}^{\circ}\) \\ Test location & Indoor \\ Lighting & Ambient light \\ Channel coding & Turbo coding \\ Modulation & BPSK \\ \hline \hline \end{tabular}
\end{table}
Table 1: Default experimental parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **w/Channel Coding** & **w/o Channel Coding** \\ \hline Input Image & Lena color (\(256\times 256\) p) & Lena color (\(256\times 256\) p) \\ No. of binary data bits & 500 & 2560 \\ No. of encoded data bits & 2518 & - \\ Lower sub-band position & rows 5 to 15 & rows 5 to 15 \\ Distance & 28 cm & 28 cm \\ Camera angle & \(0^{\circ}\) & \(0^{\circ}\) \\ Image rotation & \(0^{\circ}\) & \(0^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental parameters for channel coding.  
decoding when the data are extracted from the captured image. Conversely, a larger value of \(\alpha\) tends to degrade the image quality and the PSNR becomes low. However, it will increase the data power, which will eventually result in better communication performance. Second, we can observe that as the PSNR increases, the channels with turbo coding perform better than the channels without turbo coding. Note that we have embedded the data in the same region of the images without considering specific features of the red, green, and blue channels. We can observe that using the turbo coding, the BER remains zero until 35 dB for all the channels. In particular, we can observe that the blue channel performs a little bit worse than the green and red channels, both with and without coding. Overall, we can see that channel coding significantly affects the DFC performance. Therefore, we can conclude that channel coding is an essential part of practical DFC as without channel coding the communication is not reliable.