Semantic communication has emerged as a promising communication paradigm and there have been extensive research focusing on its applications in the increasingly prevalent multi-user scenarios. However, the knowledge discrepancy among multiple users may lead to considerable disparities in their performance. To address this challenge, this paper proposes a novel multi-pair cooperative semantic knowledge base (SKB) update policy. Specifically, for each pair endowed with SKB-enabled semantic communication, its well-understood knowledge in the local SKB is selected out and uploaded to the server to establish a global SKB, via a score-based knowledge selection scheme. The knowledge selection scheme achieves a balance between the uplink transmission overhead and the completeness of the global SKB. Then, with the assistance of the global SKB, each pair's local SKB is refined and their performance is improved. Numerical results show that the proposed cooperative SKB update policy obtains significant performance gains with minimal transmission overhead, especially for the initially poor-performing pairs.

翻译：语义通信已成为一种前景广阔的通信范式，已有大量研究聚焦于其在日益普遍的多用户场景中的应用。然而，多用户间的知识差异可能导致其性能存在显著差距。为应对这一挑战，本文提出了一种新颖的多对协同语义知识库更新策略。具体而言，对于每个配备支持SKB的语义通信的对，通过一种基于分数的知识选择方案，从其本地SKB中筛选出已充分理解的知识并上传至服务器，以建立全局SKB。该知识选择方案在上行传输开销与全局SKB的完备性之间实现了平衡。随后，在全局SKB的辅助下，各对的本地SKB得以优化，其性能也得到提升。数值结果表明，所提出的协同SKB更新策略能以最小的传输开销获得显著的性能增益，尤其对于初始性能较差的对而言效果更为明显。