Topology-Preserving Deep Joint Source-Channel Coding for Semantic Communication

• URL: http://arxiv.org/abs/2603.17126v1
• Date: Tue, 17 Mar 2026 20:40:36 GMT
• Title: Topology-Preserving Deep Joint Source-Channel Coding for Semantic Communication
• Abstract summary: TopoJSCC is a topology-aware DeepJSCC framework that integrates persistent-homology regularizers to end-to-end training.<n>We show improved topology preservation and peak signal-to-noise ratio (PSNR) in low signal-to-noise ratio (SNR) and bandwidth-ratio regimes.
• Score: 5.504022366532184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many wireless vision applications, such as autonomous driving, require preservation of global structural information rather than only per-pixel fidelity. However, existing Deep joint source-channel coding (DeepJSCC) schemes mainly optimize pixel-wise losses and provide no explicit protection of connectivity or topology. This letter proposes TopoJSCC, a topology-aware DeepJSCC framework that integrates persistent-homology regularizers to end-to-end training. Specifically, we enforce topological consistency by penalizing Wasserstein distances between cubical persistence diagrams of original and reconstructed images, and between Vietoris--Rips persistence of latent features before and after the channel to promote a robust latent manifold. TopoJSCC is based on end-to-end learning and requires no side information. Experiments show improved topology preservation and peak signal-to-noise ratio (PSNR) in low signal-to-noise ratio (SNR) and bandwidth-ratio regimes.

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