# Optical Lensless-Camera Communications Aided by Neural Network
## 4 Results and Discussion  
This section analyzes the BER performance of the proposed model. Here, we compare the BER performance of our system when using our decoding algorithm or threshold determination decoding method. The threshold determination decoding method is to compare the average gray value of an image with a fixed threshold value. If the average gray value of the image is greater than the threshold value, it will be judged as \"on\" image; otherwise, it will be judged as \"off\" image. For the indoor situation, we analyze the BER performance under different background brightness. For the outdoor situation, we analyze the decoding accuracy of our decoding algorithm on snowy channel condition. Table 3 lists the key experimental parameters. Figure 10 compares the BER performance of two cases, one in which the receiver is stationary and the other in which the receiver is slowly moving within a certain range. It can be seen from the results that the difference in BER performance between the two cases is not obvious, which indicates that our proposed decoding algorithm is not sensitive to terminal device movement. Therefore, in comparison with some image decoding algorithms, our image decoding algorithm can resist the movement of communication devices. Figure 11 shows the images of LED transmitter captured by our lensless camera when the indoor background brightness (the brightness of the \"off\" image) was around 0.5. Figure 11a is the \"off\" image of LED light without noise light source interference; Figure 11b is the \"on\" image of LED light without noise light source interference; Figure 11c is the \"on\" image of the blue LED light without  
Figure 10: Comparison of bit-error-rate (BER) performance between moving terminal device. and stationary terminal device. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Resolution & \(800\times 600\) [Pixel] \\ Camera frame rate & 30 fps \\ FOV & 78 [deg] \\ The size of sensor & 1/4 inch \\ The sensitivity of sensor & 0.6V/Lux-sec \\ Distance & 40 cm \\ Modulation frequency & \(f_{\text{ook}}=f_{\text{camera}}\) \\ Encoding & RLL \\ Lighting level & 156 Lux \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental parameters. noise light source interference; and Figure 11d is the \"on\" image of LED light with blue noise light source interference. Here, we use different colors of LED lights as transmitters to simulate the signal lights of communication devices. During the experiments, our images were captured while the lensless camera was moving slowly within a certain range, indicating we took the movement of communication devices into account. Figure 12a presents the diagram of BER using our proposed decoding algorithm. It shows that the BER is related to the indoor brightness and the length of the training sequence. Within a certain range, as the number of the training images increases, BER decreases. Moreover, normally the BER in high indoor brightness environments will be slightly higher than the BER in low indoor brightness environments. This is because the higher the indoor brightness, the more severely the image is disturbed by ambient light, e.g., when sunlight shines into a room during the day. However, in general, our decoding algorithm works well under the condition of blue noise light source interference, as seen in Figure 11d. Figure 12b shows the curves of BER using the threshold determination decoding method, of which the thresholds are different. The first threshold is set to the average gray value of all \"off\" images of the training set; the second threshold is set to the average gray value of all \"on\" images of the training set; the third threshold is set to the average of the gray value mean of all \"off\" images in the training set; and the gray value mean of all \"on\" images in the training set.