AI 中文总结
研究低延迟生成语义通信问题，提出实现耦合桥流匹配方法，通过从信道诱导语义状态初始化解码器、利用实现耦合熵最优传输计划链接训练对等，在AWGN和Rayleigh衰落信道实验中大幅降低解码延迟，实现更好的保真度 - 感知权衡。
AI 中文摘要
生成语义通信接收器能提供高感知质量，但解码延迟过高。扩散接收器依赖随机迭代解码，现有流匹配接收器采用独立端点耦合，忽略物理源 - 信道链路，导致采样轨迹过长且弯曲。本文将接收器端恢复重新表述为在明确带宽和功率约束下的实现耦合桥流匹配问题。提出实现耦合桥流匹配（RC - BFM），解码器从信道诱导的语义状态初始化。训练对通过实现耦合熵最优传输（RC - OT）计划链接。实验表明RC - BFM在保真度 - 感知权衡方面表现出色，与基于扩散的接收器相比，解码延迟降低超过10倍。
英文摘要
Generative semantic communication receivers deliver high perceptual quality but suffer from prohibitive decoding latency. This bottleneck arises because diffusion receivers rely on stochastic iterative decoding, while existing flow matching receivers employ independent endpoint coupling that ignores the physical source--channel link, yielding unnecessarily long and curved sampling trajectories. In this paper, we reformulate receiver-side recovery as a realization-coupled bridge flow matching problem under explicit bandwidth and power constraints. Specifically, we propose Realization-Coupled Bridge Flow Matching (RC-BFM), where the decoder initializes from a channel-induced semantic state rather than isotropic noise. Crucially, training pairs are linked via a realization-coupled entropic optimal transport (RC-OT) plan that preserves the physical channel realization of each transmission while maintaining robustness to stochastic fading. Furthermore, we identify independent coupling as the fundamental source of a conditional train--test distribution shift in conditional flow matching-based receivers, and derive an end-to-end distortion bound whose discretization error decays as \(O(K^{-2})\). Experiments on CIFAR-10 and FFHQ-64\(\times\)64 over AWGN and Rayleigh fading channels demonstrate that RC-BFM achieves a superior fidelity--perception trade-off, reducing decoding latency by over 10\(\times\) compared to diffusion-based receivers.
CommentsAccpeted by Globecom 2026. 6 pages, 7 figures
