The recent emergence of 6G raises the challenge of increasing the transmission data rate even further in order to overcome the Shannon limit. Traditional communication methods fall short of the 6G goals, paving the way for Semantic Communication (SemCom) systems that have applications in the metaverse, healthcare, economics, etc. In SemCom systems, only the relevant keywords from the data are extracted and used for transmission. In this paper, 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. This SemCom system is used in a setup in which the receiver allocates various categories of the same dataset collected from the transmitter, which differ in size and accuracy, to a number of users. This scenario is formulated using an optimization problem called the data allocation problem (DAP). We show that it is NP-complete and propose a greedy algorithm to solve it. Using simulations, we show that the proposed methods for SemCom system design outperform state-of-the-art methods in terms of average number of words per sentence for a given accuracy, and that the proposed greedy algorithm solution of the DAP performs significantly close to the optimal solution.

翻译：第六代移动通信（6G）技术的近期发展对传输数据速率的进一步提升提出了挑战，旨在突破香农极限。传统通信方法无法满足6G目标，这为语义通信系统的发展铺平了道路，此类系统在元宇宙、医疗、经济等领域具有应用前景。在语义通信系统中，仅从数据中提取相关关键词进行传输。本文设计了一种仅传输这些关键词的自动编码器和自动解码器，分别通过接收到的关键词和共享知识重建数据。该语义通信系统应用于以下场景：接收端将从发送端收集的同一数据集中不同大小和精度的多个类别分配给若干用户。该场景被形式化为名为数据分配问题的优化问题。我们证明该问题是NP完全的，并提出一种贪心算法进行求解。仿真结果表明，在给定精度条件下，本文提出的语义通信系统设计方法在每句平均词汇数方面优于现有最先进方法，且所提出的数据分配问题贪心算法解与最优解性能非常接近。