# Distinction of Scrambled Linear Block Codes Based on Extraction of Correlation Features
## 5 Results
### Experimental Results  
#### 5.3.1 Recognition Rate of The Proposed Algorithm under Bit Error Condition  
To verify the validity of the model proposed in this paper, we analyzed the influence of the linear block code parameters, the number of intercepted samples, and the order and terms of the generator polynomial on the network performance under different error conditions.  
The relationship between the parameters of the different linear block codes and the recognition rate of the algorithm when the generator polynomial is \(f(x)=1+x^{18}+x^{23}\) and the number of samples is 400 is shown in Figure 11.  
It can be seen in the figure that, under the same simulation conditions, the (15,5) linear block code has the highest recognition rate. At that time, the recognition rate under the four coding parameters can reach more than 85%, indicating good anti-error performance.  
The (15,5) linear block code scrambling recognition rate under different sample numbers when the generator polynomial is \(f(x)=1+x^{18}+x^{23}\) is shown in Figure 12.  
It can be seen in the figure that, under the same error conditions, more samples mean a higher recognition rate of the algorithm. When the number of samples is 100 and the error rate is 0.2, the recognition rate is greater than 80%. When the error rate is 0.1, the recognition rate can reach 98%, which shows that the proposed algorithm has better performance under the conditions of a small number of samples and a high BER.  
The (15,5) linear block code scrambling recognition rate under different generator polynomials for 400 samples is shown in Figure 13.  
Figure 10: The effect of the number of cells of CNN on the performance of the algorithm.  
Figure 9: Effect of the number of CNN layers on the performance of the algorithm.  
It can be seen in Figure 13 that, with an increase in the bit error rate, the recognition rate gradually decreases. The difference in the recognition rate under a generator polynomial of different orders and terms is not more than 1%. Therefore, the algorithm proposed in this paper is free from the scrambling code generator polynomial order.  
The performance of the algorithm proposed in this paper under different linear block code parameters is compared with that of the method proposed in Refs [26; 27]. When  
Figure 11: Influence of linear block code parameters on the recognition rate.  
Figure 12: Influence of number of samples on the recognition rate.  
Figure 13: Influence of scrambling code generator polynomial on the recognition rate.  
the generator polynomial is \(f(x)=1+x^{18}+x^{23}\), the number of samples is 400, and the sequence length is 240,000 bits as shown in Figure 10.  
It can be seen in Figure 14 that the recognition rate of the algorithm proposed in this paper when the number of verification samples is only 400 is better than the recognition rate of the algorithm in Reference [26] when the number of samples is 240,000 bits. The recognition rate of the algorithm in Reference [27] decreases rapidly and tends to be 0 when the BER is greater than 0.01. The reason for this is that the rank feature of the matrix is not obvious when the BER is greater than 0.01, which leads to a sharp decrease in the recognition rate. At the same time, the proposed algorithm does not require manual feature extraction and has the characteristics of less data required, simple manual feature extraction, and a high recognition rate.