# Large-Language-Model-Enabled Text Semantic Communication Systems
## 4 Numerical Results
### The Performance of BER and TER  
To validate the effectiveness and reliability of the LLM-SC, we also conducted a comparison with traditional technical communication systems. Based on the observations from the previous subsection, the performance of the LLM-SC under AWGN and Rayleigh fading channels is comparable; therefore, the simulations in this subsection were conducted only under AWGN channels. Unlike BLEU and sentence similarity, which are often used for evaluating semantic communication, both LLM-SC and classical algorithms utilize classical modulation schemes such as QAM and QPSK. Therefore, we compared the BER and TER under the same modulation scheme, allowing us to assess system performance under identical modulation and joint coding efficiency conditions. In this simulation, we employed the commonly used 16-QAM modulation scheme for a fair comparison. To ensure a fair comparison, we first calculated the equivalent joint source-channel coding rate of LLM-SC using the metric bits per symbol (bpc), which represents the number of bits used for each character transmitted through the channel. In technical communication systems, this value is typically the product of the source coding rate and the channel coding rate. Hence, we compute the average equivalent joint source-channel coding rate for LLM-SC. Based on simulation data presented in Table 2, the equivalent joint coding rate of LLM-SC is approximately \(R\approx\frac{78.93\times 16}{348.87}\approx 3.62\) (bpc). The factor of 16 arises from the use of a vocabulary size of 32,000, necessitating at least 15 bits for representation, along with the employment of 16-QAM modulation. Consequently, in a high-dimensional constellation, at least four modulation symbols are required to represent a token. Since 16-QAM carries 4 bits per modulation symbol, 16 bits are needed per token to be encoded. The approximation (\(\approx\)) indicates  
Figure 3: Sentence similarity versus SNRs for the same number of bits per transmitted symbol, with Huffman coding with RS (30,42) in 64-QAM, 5-bit coding with RS (42,54) in 64-QAM, Huffman coding with Turbo coding in 64-QAM, 5-bit coding with Turbo coding in 128-QAM, Brotli coding with Turbo coding in 8-QAM, the DNN-based JSCC trained over AWGN channels and Rayleigh fading channels [44], DeepSC trained over the AWGN channels and Rayleigh fading channels [5], and finally, our proposed LLM-SC. that this rate is derived from a large number of sentences, and may vary across different sentences. This detailed analysis ensures a rigorous comparison of the LLM-SC system against classical communication systems under the same modulation conditions, providing insights into their relative performance in terms of BER and TER. To benchmark against traditional technical communication systems, we calculate the coding rate of commonly used source coding methods, such as Huffman coding, zlib, and arithmetic coding (AC), also expressed in bpc, as shown in Table 3.  
As shown in Table 3, the joint coding efficiency of LLM-SC surpasses that of conventional text compression algorithms. Notably, LLM-SC maintains semantic-level redundancy, which enhances error correction capabilities at the receiver. In contrast, for technical communication systems, achieving the same bpc precludes the incorporation of additional channel coding schemes, as these would increase the bpc. Nevertheless, even without considering channel coding, the bpc of technical systems remains higher to that of LLM-SC. Therefore, for subsequent comparisons, channel coding schemes will be excluded for technical communication systems. We evaluated the demodulation performance by transmitting English text samples from the dataset, comparing LLM-SC in 16-QAM with UTF-8 encoding (hard demodulation) in 16-QAM. The metrics used for evaluation were BER and TER, as tokens represent the fundamental unit of transmission and reception. The beam size utilized in the simulation was 15. The results, presented in Figure 4, demonstrate that LLM-SC significantly outperforms hard demodulation.