Multi-User Information Bottleneck for Semantic-Aware Communication
Cheng Peng, Yujie Zhou, Rulong Wang, Yong Xiao, Yingyu Li, Guangming Shi · 2025
Semantic-aware communication (SAC) has attracted significant interest recently due to its potential to revolutionize the traditional communication framework by focusing on delivering the meaning of information, enabling more efficient, reliable, and intelligent communication. Most existing AI-based solutions for multi-user SAC focus on developing encoding models for all users and a decoding model for a specific receiver. While powerful, this single-task-oriented codec design faces significant challenges when deployed in more general multi-task scenarios. In this paper, we investigate codec design problems in multi-user multitask SAC based on distributed information bottleneck (DIB) theory. We propose a novel task-aware DIB scheme (TADIB) for joint codec designing. In TADIB, the receiver, when performing a specific task, first estimates the relevance between users' datasets and the task based on their mutual information, which is incorporated into the training phase of both encoders and decoders. Then most task-relevant users are selected to send the encoded signals for task inference during the deployment phase. Furthermore, we employ variational approximation to derive tractable upper bounds for the DIB-based objective, which would otherwise be computationally prohibitive for high-dimensional data. This approach also reduces the computational complexity of the user selection procedures. Extensive results show that the proposed TADIB can achieve up to 3.38% improvements in inference accuracy of classification tasks, compared to existing solutions for task-oriented SAC.