The recent emergence of 6G raises the challenge of increasing the transmission data rate even further in order to break the barrier set by the Shannon limit. Traditional communication methods fall short of the 6G goals, paving the way for Semantic Communication (SemCom) systems. These systems find applications in wide range of fields such as economics, metaverse, autonomous transportation systems, healthcare, smart factories, etc. In SemCom systems, only the relevant information from the data, known as semantic data, is extracted to eliminate unwanted overheads in the raw data and then transmitted after encoding. In this paper, we first use the shared knowledge base to extract the keywords from the dataset. Then, we design an auto-encoder and auto-decoder that only transmit these keywords and, respectively, recover the data using the received keywords and the shared knowledge. We show analytically that the overall semantic distortion function has an upper bound, which is shown in the literature to converge. We numerically compute the accuracy of the reconstructed sentences at the receiver. Using simulations, we show that the proposed methods outperform a state-of-the-art method in terms of the average number of words per sentence.

翻译：随着第六代移动通信（6G）的兴起，如何进一步提升数据传输速率以突破香农极限成为关键挑战。传统通信方式难以满足6G目标，从而催生了语义通信系统。这类系统广泛应用于经济学、元宇宙、自动驾驶交通系统、医疗健康、智能工厂等诸多领域。在语义通信系统中，仅提取数据中的相关信息（即语义数据）以消除原始数据中的非必要开销，并在编码后传输这些语义数据。本文首先利用共享知识库从数据集中提取关键词，进而设计一种自编码器与自解码器，仅传输这些关键词，并基于接收到的关键词与共享知识恢复数据。我们从理论上证明，整体语义失真函数存在一个上界，现有文献已证明该上界收敛性。通过数值计算，我们评估了接收端重构语句的准确率。仿真结果表明，就平均每句包含的单词数而言，所提方法优于当前最先进的方法。