MaskDSC: Resilient Digital Semantic Communication with Masked Transformer and Unequal Error Protection

We propose “MaskDSC”, a novel system designed to facilitate robust visual data transmission over unreliable wireless channels. MaskDSC effectively balances compression efficiency and transmission resilience by leveraging contextual modeling within the semantic latent space, complemented by unequal error protection mechanism at the physical layer, ensuring compatibility with existing digital communication systems. The novelty of our approach lies in a dual-functional masked Transformer architecture that exploits causal-order contextual dependencies among visual tokens. This architecture not only enhances compression efficiency through improved contextual entropy modeling but also provides robust error concealment capabilities to address diverse transmission error patterns inherent in volatile wireless channels. Our experimental evaluations conducted on image datasets demonstrate that MaskDSC outperforms state-of-the-art transmission systems, especially in terms of efficiency and resilience under dynamic wireless channel conditions.