## 4 System Architecture and Performance Analysis
### Performance Analysis
#### 4.3.2 Performance Analysis  
(a) Blur kernel size  
To analyze the effect of blur kernel size on data decoding quality, we performed a simulation analysis on several sets of images which are blurred by different blur kernel sizes. Figure 9 shows the testing performance of decoding data from images which are non-blurry, blurred by a \(40\times 40\) kernel, and blurred by an \(80\times 80\) kernel using two different methods: A general PSK scheme decoding technique using a matched filter and our proposed technique using an NN. The dimming level is set to 4/8 or 50% throughout this simulation. Figure 8: Illustration of data augmentation with the blur process and noisy channel for neural network-based decoder training and performance evaluation. Figure 9: The decoding performance analysis of the matched filter and neural network-based decoder on blurred and noisy images (difference blur kernel, AWGN channel). The results of this experiment reveal that our NN-based decoder improves the decoding SER more significantly than the general decoding technique using a matched filter. Generally, our target SER value for the feasibility of a vehicular OCC system is \(10^{-4}\). We analyzed the required SNR and achieved this target value. In the non-blur case, to achieve an SER below \(10^{-4}\), the required SNR of our NN decoding must be improved by 2 dB, in contrast to using a matched filter. With a blur kernel size of \(40\times 40\), the improvement on the SNR of the NN-based decoding is approximately 2.2 dB. However, with a blur kernel size of \(80\times 80\), our decoder achieved the target SER value at an SNR of 40 dB, and the matched filter achieved an SER of \(10^{-3}\) at the same SNR value. Notably, the improvement of the required SNR increases when the blur kernel size is increased (see Figure 9). The difference in our data preprocessing technique is attributed to the key factor. From each image of the transmitter LED group, we extracted central point intensities of eight LEDs in each group and translated them to fuzzy logic states of 16 LEDs (two groups) for input features of the NN-based decoder. For the AWGN channel feature, we used the standard deviation value \(\delta\), which can be measured and calculated from the SNR value utilized in [8]. From Figure 7, besides the LED states and the standard deviation of Gaussian noise, we propose new features which reflect the dimming cases and blur kernel property, calculated as follows:  
\[f_{bl}=dimming\frac{kernel\_area}{image\_area} \tag{10}\]  
Here, the dimming value is from 1 to 7, kernel_area is the number of pixels that the blur kernel occupied on the LED image, and image_area is the total number of pixels on the LED image (\(200\times 100\) pixels). It is worthy of note that the kernel area in this equation is calculated on the central point of each LED in the LED group, which is clearly illustrated in Figures 10 and 11. Figure 11: The calculation of the ratio of the blur kernel area in the image with kernel size = \(60\times 60\). Figure 10: The calculation of the ratio of the blur kernel area in the image with kernel size = \(40\times 40\). In Figure 10, we considered the case where the blur kernel size does not exceed the size of one LED area on the image (\(50\times 50\) in this case). Therefore, when calculating the after-blurred intensity of the LED central point, the whole area of a blur kernel will be inside the cropped image of the LED group. In Figure 11, the size of a blur kernel (\(60\times 60\)) exceeds the size of one LED area on the image (\(50\times 50\)), so the exact area of the blur kernel inside a cropped image for calculating the after-blurred intensity of the LED central point will be part of the blur kernel. (b) Dimming  
Dimming level is also one of the essential features that directly affect the decoding performance of a communication system, especially when the blur effect is occurring. This impact could be significant or trivial depending on the blur kernel size.