面向多对语义通信的协作式语义知识库更新策略
Cooperative Semantic Knowledge Base Update Policy for Multiple Semantic Communication Pairs
AI总结：
针对多用户语义通信中知识差异导致性能不均衡的问题，本文提出基于评分知识选择的多对协作式语义知识库更新策略，通过构建全局SKB并精炼本地SKB，以极小传输开销显著提升各用户对性能。
摘要：
语义通信已成为一种极具前景的通信范式，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，多用户之间的知识差异可能导致其性能出现显著差距。为应对这一挑战，本文提出了一种新颖的多对协作式语义知识库（SKB）更新策略。具体而言，对于每对具备SKB使能语义通信能力的用户，通过一种基于评分的知识选择方案，将其本地SKB中已被充分理解的知识挑选出来并上传至服务器，以构建全局SKB。该知识选择方案在上行传输开销与全局SKB的完备性之间取得了平衡。随后，借助全局SKB的辅助，每对用户的本地SKB得到精炼，其性能得以提升。数值结果表明，所提出的协作式SKB更新策略以极小的传输开销获得了显著的性能增益，尤其是对于初始性能较差的用户对而言。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
