# Large-Language-Model-Enabled Text Semantic Communication Systems
## 4 Numerical Results  
Simulations have been conducted to assess the capability of LLM-SC, focusing on measuring feasibility rather than extensive benchmarking. From the discussions above, it is concluded that using existing LLMs allows for the verification of the feasibility of the proposed method, while the vocabulary size \(V\) remains fixed. Adjusting the modulation order provides a means to control communication efficiency. In this section, we present the existing LLMs that we employed without fine-tuning or additional training to verify the feasibility of LLM-SC. We present a comprehensive comparison of existing semantic communication schemes among multiple evaluation metrics and discuss factors influencing their performance. Notably, this method accounts for the technical communication context, enabling restoration of original characters and facilitating comparison with traditional communication systems in terms of BER, a challenging metric to compare all semantic communication systems.  
The dataset consists of English text extracted from the Europarl proceedings [41], widely utilized in semantic communication tasks. Due to computational constraints, we conducted preprocessing to eliminate extraneous characters and filter out sentences that were excessively short or long. The LLM utilized in our simulations was Vicuna (v1.5), fine-tuned from LLaMA. Vicuna-7B v1.5 is chosen for its balance of efficiency and performance. Fine-tuned from LLaMA on conversational data, it achieves 90% of ChatGPT's quality on MT-Bench [42]. Its 4,096-token context window supports long-sequence decoding, and the 32,000-token vocabulary covers diverse linguistic constructs in the Europarl corpus. In fact, we can use any existing LLM to conduct experiments.  
AWGN and Rayleigh fading channels were selected for their fundamental representation of real-world scenarios. The AWGN channel models ubiquitous thermal noise, while Rayleigh fading captures multipath effects in non-line-of-sight environments (e.g., urban areas). These models are standardized benchmarks in 3 GPP specifications [43]. Table 2 outlines the key simulation settings.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline LLM Model & Vicuna 7b v1.5 \\ \hline Dataset & European Parliament Proceeding \\ \hline CPU & 16 vCPU AMD EPYC 9654 96-Core Processor \\ \hline GPU & NVIDIA RTX 4090 \\ \hline Average number of characters of sent text (100,000 samples) & 348.87 \\ \hline Average number of tokens in sent text & 78.93 \\ \hline SNR (dB) & 2–20 \\ \hline Temperature & 1.0 \\ \hline Beam size & 1–30 \\ \hline Modulation scheme & 8-QAM/16-QAM \\ \hline Vocabulary size & 32,000 \\ \hline The number of bits per token & 15, 16 \\ \hline Channel model & AWGN/Rayleigh \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation settings.  
We conducted simulations using an NVIDIA RTX 4090 for performance evaluation. Specifically, 16 QAM and 8-QAM modulations were adopted for comparisons. The choice of 16 QAM facilitated comparisons with traditional communication systems, while 8-QAM was selected for comparison against the findings in the literature [5]. Our simulations encompassed AWGN and Rayleigh fading channel models, and include multiple sets of comparative analyses to assess the influence of beam size.