Generative semantic communication (SemCom) harnesses pretrained generative priors to improve the perceptual quality of wireless image transmission. Existing generative SemCom receivers, however, rely on maximum a posteriori (MAP) estimation, which fundamentally cannot preserve the data distribution and thus limits achievable perceptual quality. Moreover, current diffusion-based approaches using single-domain guidance face significant limitations: latent-domain guidance is sensitive to channel noise, while image-domain guidance inherits decoder bias. Simply combining both domains simultaneously yields an overconfident pseudo-posterior. In this paper, we formulate semantic decoding as a Bayesian inverse problem and prove that posterior sampling achieves optimal perceptual quality by preserving the data distribution. Building on this insight, we propose alternating dual-domain posterior sampling (ADDPS), a diffusion-based SemCom receiver that alternately enforces latent-domain and image-domain consistency during the sampling process. This alternating strategy decomposes joint posterior sampling into simpler subproblems, avoiding gradient conflicts while retaining the complementary strengths of both domains. Experiments on FFHQ demonstrate that the proposed ADDPS achieves superior perceptual quality compared with existing methods.

翻译：生成式语义通信利用预训练生成先验提升无线图像传输的感知质量。然而，现有生成式语义通信接收机依赖最大后验估计，从根本上无法保持数据分布，从而限制了可达感知质量。此外，当前使用单域引导的扩散方法面临显著局限：潜在域引导对信道噪声敏感，而图像域引导则继承了解码器偏差。简单组合双域引导会导致过度自信的伪后验。本文中，我们将语义解码建模为贝叶斯逆问题，并证明后验采样可通过保持数据分布实现最优感知质量。基于此洞见，我们提出交替双域后验采样（ADDPS），这是一种基于扩散的语义通信接收机，在采样过程中交替施加潜在域与图像域的一致性约束。该交替策略将联合后验采样分解为更简单的子问题，既避免梯度冲突，又保留双域的互补优势。在FFHQ数据集上的实验表明，与现有方法相比，所提ADDPS实现了更优的感知质量。