Robust Multimodal Information Bottleneck for Satellite-to-Ground Task-Oriented Communication

In this paper, we study satellite-to-ground taskoriented communication for edge inference tasks, where a satellite extracts, fuses and encodes multimodal feature vectors and then sends them to a ground server under inevitable channel noise conditions for downstream processing. However, the multispectral and multi-resolution characteristics of multimodal satellite remote sensing data render traditional multimodal methods inapplicable. To reduce the data redundancy caused by the high-dimensional and complex multimodal vectors generated onboard while retaining key information and enhancing robustness against channel noise. We propose a Robust Multimodal Information Bottleneck (RMIB) framework which considers channel noise and communication bandwidth and introduces a new information bottleneck optimization objective. By applying this objective through end-to-end training, we optimize the feature extraction, fusion and encodes multimodal data into robust and effective feature vector in noisy communication environments by reducing redundancy and enhancing feature discrimination. To tackle the RMIB objective function, we derive a tractable variational upper bound using the Variational Information Bottleneck technique to overcome the computational intractability of mutual information. Experimental results demonstrate that our method not only outperforms baseline techniques in classification accuracy on three datasets but also enhances robustness against channel noise and reduces communication overhead.