  Large language models (LLMs) have recently demonstrated state-of-the-art
performance across various natural language processing (NLP) tasks, achieving
near-human levels in multiple language understanding challenges and aligning
closely with the core principles of semantic communication. Inspired by LLMs'
advancements in semantic processing, we propose an innovative LLM-enabled
semantic communication system framework, named LLM-SC, that applies LLMs
directly to the physical layer coding and decoding for the first time. By
analyzing the relationship between the training process of LLMs and the
optimization objectives of semantic communication, we propose training a
semantic encoder through LLMs' tokenizer training and establishing a semantic
knowledge base via the LLMs' unsupervised pre-training process. This knowledge
base aids in constructing the optimal decoder by providing the prior
probability of the transmitted language sequence. Based on this foundation, we
derive the optimal decoding criterion for the receiver and introduce the beam
search algorithm to further reduce the complexity. Furthermore, we assert that
existing LLMs can be employed directly for LLM-SC without additional
re-training or fine-tuning. Simulation results demonstrate that LLM-SC
outperforms classical DeepSC at signal-to-noise ratios (SNR) exceeding 3 dB,
enabling error-free transmission of semantic information under high SNR, which
is unattainable by DeepSC. In addition to semantic-level performance, LLM-SC
demonstrates compatibility with technical-level performance, achieving
approximately 8 dB coding gain for a bit error ratio (BER) of $10^{-3}$ without
any channel coding while maintaining the same joint source-channel coding rate
as traditional communication systems.
