This paper proposes semantic interference cancellation (SemantIC)-Mamba, a novel framework that enhances semantic reconstruction accuracy while stably maintaining channel decoding performance. The proposed model follows a turbo structure in which information is complementarily exchanged between the semantic domain and the signal domain, progressively refining the reconstruction quality through iterative processing. To effectively perform this iterative information refinement, the proposed framework adopts a semantic autoencoder composed of three key components: a Conv block that extracts local features, a Mamba block that efficiently models long-range dependencies to integrate global contextual information, and an UpConv block that restores low-resolution features to the original resolution. Experimental results demonstrate that SemantIC-Mamba consistently achieves improved PSNR and classification accuracy compared to conventional SemantIC and SemantIC++ while maintaining channel decoding performance at a level comparable to existing models.
Yoon et al. (Mon,) studied this question.