Multi-View Semantic-Aware Communication: An Information Bottleneck Perspective

This paper investigates the joint source-channel coding (JSCC) problem for semantic-aware communication (SAC) systems with multi-view data sources. We extend the theory of multi-view information bottleneck by incorporating a constraint that characterizes the impact of channel corruption on the recovery of task signals at the receiver, referred to as the multiview information bottleneck with channel protection (MVIB-CP). We propose a computationally efficient optimization framework for calculating the MVIB-CP problem in which a contrastive log-ratio upper bound and a simplified variation approximation solution are introduced to estimate mutual information and approximate the divergence of task signal recovery caused by the noisy channel corruption. The proposed MVIB-CP can investigate the tradeoff of the multi-view source compression and the protection of channel corruptions while the above optimization framework can significantly reduce the computational complexity of addressing the MVIB-CP. We present the optimization algorithm for developing the JSCC schemes in multi-view SAC systems. Extensive experiments are conducted based on realworld multi-view source datasets. Our results show that the MVIB-CP-based JSCC achieves up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{5. 2 8 \%}$</tex> improvement in task signal recovery accuracy, compared to the classical JSCC solution.