# Design of an SVM Classifier Assisted Intelligent Receiver for Reliable Optical Camera Communication
## 5 Experimental Results
### BER, Data Rate, and ISI Analysis  
After classifying accurate data transmitting LED, data bits have been decoded from the cropped region using a decoding algorithm described in Algorithm 1. We have decoded data and evaluated the communication performance of our proposed intelligent receiver. According to our scheme, the receiver intelligently selects accurate LED regions so the probability of interference from other sources or reflection from the surrounding surface have been reduced. Figure 10 depicts the measured BER performance against communication distance for the receiver employing only an SVM classifier, only CNN, and our proposed scheme. A significant reduction in BER has been observed when the SVM classifier along with CNN is used. At the maximum distance of 60 cm, the BER increases up to \(7.9\times 10^{-4}\), which is below the forward error correction (FEC) limit of \(3.8\times 10^{-3}\). At this point, only the SVM based receiver shows BER above the FEC limit, whereas the CNN based receiver is very close to the FEC limit. It signifies the effectiveness of our proposed scheme.  
The BER performance varies based on exposure time. The effect of changing exposure time \(T_{e}\) on BER for a single data transmitting LED keeping two unwanted LED regions has also appeared in Figure 11a. With the increase of exposure time, BER also increases due to the interfering lights from other sources. Although BER has increased with the increase of the exposure time \(T_{e}\), the maximum BER at the maximum distance is below the FEC limit in our scheme. We have also investigated BER performance by varying the number of LEDs, as shown in Figure 11b. When the amount of actual data transmitting LED increases, efficient separation of LED regions has an enormous influence on BER. Therefore, a significant change in BER has been found when the amount of data transmitting LED has been increased from one to three. When the amount of data transmitting LED is three, the BER has been achieved around \(3.5\times 10^{-3}\) for \(T_{e}=976.6\) us.  
Furthermore, the eye diagram depicted in Figure 12 at \(T_{e}=976.6\) us has ensured the low ISI at the receiver side. During multiple data transmitting LED region cases, if the receiver fails to differentiate distinct regions accurately, recovering a distinct pulse for a distinct bit can be difficult for the receiver. The large eye opening affirms the low ISI presence at separated regions classified by the SVM classifier.  
Since the SVM classifier separates LED regions and data are decoded from the separated regions, it has a significant effect on data rate too. When communication distance is changed, some low intensity stripes may cancel out from the detected regions. A distinctive change in data rate is caused due to the change in no. of stripes in the LED regions. With the change in no. of LEDs, data rate variation against communication distance is shown in Figure 13. For a single LED, the maximum estimated data rate is 960 bps at distance  
Figure 10: Comparison of bit error rate (BER) among receivers using only SVM, only CNN, and our proposed scheme.  
However, some stripes can be recovered further by updating in the image processing steps at a fixed distance. It causes a change in the no. of erroneous and accurately decoded bits. Therefore, a significant change in the data rate and BER have been observed. Figure 14 illustrates BER vs. data rate performance while communication distance was 50 cm for three different cases. At each case, two LEDs were in data transmitting mode, and one was used as an unwanted region in the image frame. For achieving 960 bps, BER recorded for SVM-based receiver is beyond the FEC limit, while the CNN-based receiver shows more closeness to the FEC limit. Our scheme-based receiver can decode data at 960 bps at a BER of \(3.5\times 10^{-3}\).