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

Omar Erak, Omar Alhussein, Fang Fang, Sami Muhaidat

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.

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

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.
Paper Structure (11 sections, 11 equations, 4 figures, 1 table)

This paper contains 11 sections, 11 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model
  3. Proposed Topology-Aware DeepJSCC
  4. Image-Domain Topological Loss
  5. Latent-Space Topological Loss
  6. Final Training Loss
  7. Choice of Topological Hyperparameters
  8. Results and Discussion
  9. Experimental Setup
  10. Experimental Results
  11. Conclusion

Figures (4)

  • Figure 1: TopoJSCC system model and losses
  • Figure 2: Omniglot results under an SNR sweep. Bandwidth ratio is fixed at 0.40 and Topology-regularized DeepJSCC exhibits the most pronounced improvements in the low-SNR regime under both AWGN and Rayleigh fading.
  • Figure 3: DeepGlobe Road Extraction dataset results under a bandwidth-ratio sweep. SNR is fixed at 15 dB. Topology-regularized DeepJSCC yields larger topology improvements at low bandwidth ratios under both AWGN and Rayleigh fading.
  • Figure 4: Reconstructions showing topological preservation at SNR = 5 dB.