Optimally Bridging Semantics and Data: Generative Semantic Communication via Schrödinger Bridge

2026-04-20

AI Summary

This work addresses the limitations of existing generative semantic communication methods, which rely on Gaussian priors and consequently suffer from severe hallucinations and high computational overhead in narrowband, high-noise channels. To overcome these issues, the authors propose a Schrödinger Bridge-based Generative Semantic Communication (SBGSC) framework that dispenses with Gaussian assumptions by constructing an optimal transport trajectory between semantic and image distributions, enabling direct generative decoding. By reformulating the nonlinear drift term of diffusion models and introducing a self-consistent guidance strategy for non-Markovian generation, SBGSC effectively learns the underlying velocity field, drastically reducing sampling steps while suppressing hallucinatory artifacts. Experimental results demonstrate that SBGSC improves the Fréchet Inception Distance (FID) by at least 38%, increases Structural Similarity Index (SSIM) by 49.3%, and accelerates inference by over eightfold compared to current state-of-the-art methods.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Generation
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Generative Semantic Communication (GSC) is a promising solution for image transmission over narrow-band and high-noise channels. However, existing GSC methods rely on long, indirect transport trajectories from a Gaussian to an image distribution guided by semantics, causing severe hallucination and high computational cost. To address this, we propose a general framework named Schrödinger Bridge-based GSC (SBGSC). By leveraging the Schrödinger Bridge (SB) to construct optimal transport trajectories between arbitrary distributions, SBGSC breaks Gaussian limitations and enables direct generative decoding from semantics to images. Within this framework, we design Diffusion SB-based GSC (DSBGSC). DSBGSC reconstructs the nonlinear drift term of diffusion models using Schrödinger potentials, achieving direct optimal distribution transport to reduce hallucinations and computational overhead. To further accelerate generation, we propose a self-consistency-based objective guiding the model to learn a nonlinear velocity field pointing directly toward the image, bypassing Markovian noise prediction to significantly reduce sampling steps. Simulation results demonstrate that DSBGSC outperforms state-of-the-art GSC methods, improving FID by at least 38% and SSIM by 49.3%, while accelerating inference speed by over 8 times.

Problem

• Generative Semantic Communication
• Hallucination
• Computational Cost
• Optimal Transport
• Image Transmission

Innovation

• Generative Semantic Communication
• Schrödinger Bridge
• Optimal Transport
• Diffusion Models
• Self-consistency