# Reliable Semantic Communication System Enabled by Knowledge Graph
## 4 Experimental Results
### Experimental Result Analysis
#### 4.2.1 Performance of the Proposed Semantic Communication System
From Figure 9, \"Sentence Length (0, 15)\" is significantly higher than the other two groups. This is because the proposed model only transmits the most important triplet in the low SNR, and the length of the restored sentence is limited. In the low SNR region, the BLEU score decreases as the sentence length increases. With the SNR increasing, the number of the transmitted triplets increases, and the gaps between the different groups narrow. In Figure 9, the gaps between the different groups are not obvious due to the effects of Rayleigh fading. Figure 8: BLEU score versus the SNR over the Rayleigh fading channel. (a) BLEU(1-gram) score over the Rayleigh fading channel. (b) BLEU(2-gram) score over the Rayleigh fading channel. Figure 7: BLEU score versus the SNR over the AWGN channel. (a) BLEU(1-gram) score over the AWGN channel. (b) BLEU(2-gram) score over the AWGN channel. Figure 10 shows the METEOR score versus the SNR over the AWGN channel and the Rayleigh fading channel. From Figure 10, the score of the benchmark is close to 1 and higher than our proposed model when the SNR is above 4 dB. This is because the few errors that occurred during the transmission were corrected by the channel coding at a high SNR; the benchmark could restore the transmitted sentence without distortion. However, our proposed model discards the information of sentence structure during transmission. When the SNR is less than 4 dB, the channel coding cannot correct all errors during transmission. In this situation, the METEOR score of the benchmark degrades rapidly. However, the proposed model reduces the number of transmitted triplets and protects important triplets in this case, which leads to a better performance in the low SNR region. From Figure 10, even under the Rayleigh fading channel, our model outperforms the benchmark in all SNR regions. Figure 11 draws the relationship between the SNR and the semantic similarity under the AWGN channel and the Rayleigh fading channel. From Figure 11, the \"Proposed model without AT and SR\" outperforms the benchmark in the low SNR region under the AWGN channel, while it outperforms the benchmark in all SNR regions under the Rayleigh fading channel. This is because our proposed model splits the transmitted sentence into multiple independent triplets, leading to that, the wrongly transmitted triplets will not affect the semantics of other triplets. However, the benchmark model transmits the sentence as a whole, and if errors occur in the transmission, then the semantics of the sentence are affected. Therefore, when the channel quality is poor, our proposed model can preserve partially correct semantics. Meanwhile, since the semantic similarity based on the BERT  
Figure 10: (**a**) METEOR score versus the SNR over the AWGN channel. (**b**) METEOR score versus the SNR over the Rayleigh fading channel. Figure 9: BLEU (1-gram) score versus the SNR with sentence length (0, 15). Sentence Length (15, 30); sentence length (>30). (**a**) BLEU (1-gram) score over the AWGN channel. (**b**) BLEU (1-gram) score over the Rayleigh fading channel. model can capture semantic relationships among words, the proposed scheme obtains a higher similarity compared with the BLEU score and METEOR score. To ensure the fairness of the comparison of experimental results, we computed the time complexities of all strategies. We transmitted 1000 sentences from the transmitter to the receiver by using different strategies and calculated the average execution time. All tests were run on Python and were performed by the computer with AMD Ryzen 7 4800H and NVIDIA GeForce GTX 3060. The results are shown in Table 4. From Table 4, our proposed model increases the computational complexity and improves communication reliability.