Cooperative Semantic Knowledge Base Update Policy for Multiple Semantic Communication Pairs

TL;DR

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.

Abstract

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.

Figures (5)

• Figure 1: System model and cooperative SKB update process.
• Figure 2: $M_l$ versus $\gamma$.
• Figure 3: Transmission latency versus $\gamma$.
• Figure 4: $F_1$-scores of each class. Blue bars and orange bars represent the $F_1$-scores without cooperation and with cooperation, respectively.
• Figure 5: Visual comparisons of the generated images.