# On Improving 5G Internet of Radio Light Security Based on LED Fingerprint Identification Method
## 3 Demonstration and Evaluation
### Results and Analysis  
Figure 5 illustrates 4 LEDs' distribution of the EVM in the whole area. According to the results, LED-A and LED-C had the largest and the second-largest coverage of the high-quality communication environment. On the contrary, the channel of the LED  
Figure 4: (**a**) A flow chart of the demonstration; (**b**) The realistic scenario; (**c**) The structure of 5G NR signal.  
B and LED-D had limited quality. The phenomenon that the same type of four LEDs has different communication performance was caused by the non-linearity of the optical wireless channel.  
Based on this environment, the total of 960 samples were captured by the receiver and, based on them, the LED fingerprints shown in Figure 6a were extracted. The extraction results were put into selected machine-learning algorithms, i.e., Decision Tree, Naive Bayes, SVM and KNN classifiers, respectively. The comparison of the verification accuracy is presented in Figure 6b. According to the obtained results, it is clearly visible that the proposed extraction method successfully extracted the LED fingerprints of four LEDs. Meanwhile, four machine-learning-based classifiers are able to distinguish these fingerprints appropriately.  
Figure 6: (**a**) Extracted LED fingerprints; (**b**) Comparison of verification accuracy.  
Note that further investigation leads to the conclusion that the fingerprints of the LED-B and LED-D overlapped in some regions, which was the primary reason of the classifiers' misjudgment. In this case, the SVM classifier obtained the best performance due to its property of maximum classification margin and its ability of producing accurate and robust classification results for non-monotone and non-linearly separable input data. Moreover, the confusion matrix and parallel coordinate plots of the SVM classifier for the extraction and identification results are presented in Figure 7a,b.  
According to the confusion matrix, almost all the misjudgment is caused by the LED-B as 8.4% LED fingerprints of LED-B were regarded as the fingerprints of the LED-D. Meanwhile, 2.1% fingerprints of the LED-D were misclassified as the fingerprints of the LED-B. Additionally, the parallel coordinate plots reflected the distinction of each parameter for all the fingerprints. The LED-A, LED-B and LED-C had distinct convergence on the parameter C. On the contrary, the LED-D performed more divergently and overlapped with the LED-B. For the parameter \(\eta\), all the LEDs were convergent; however, the LED-B, LED-C and LED-D overlapped in a small interval. This phenomenon can be explained as follows. First, the identification quality is influenced by the communication environment. The LED-A has the best EVM and covered almost the whole area, which caused the highest identification accuracy. On the contrary, the LED-B yielded the worst accuracy due to its limited EVM coverage. Secondly, the parameter \(\eta\) is sensitive to the communication environment. According to the theoretical analysis of the extraction, \(\eta\) represents the photoelectric efficiency. Once there is a large attenuation in the channel, then the \(\eta\) will be calculated near the boundary value. Consequently, it will lose the ability to describe the characteristics of the LED.