Deep generative models offer a powerful alternative to conventional channel estimation by learning the complex prior distribution of wireless channels. Capitalizing on this potential, this paper proposes a novel channel estimation algorithm based on latent diffusion models (LDMs), termed posterior sampling with latent diffusion for channel estimation (PSLD-CE). The core of our approach is a lightweight LDM architecture specifically designed for channel estimation, which serves as a powerful generative prior to capture the intricate channel distribution. Furthermore, we enhance the diffusion posterior sampling process by introducing an effective approximation for the likelihood term and a tailored self-consistency constraint on the variational autoencoder latent space. Extensive experimental results demonstrate that PSLD-CE consistently outperforms a wide range of existing methods. Notably, these significant performance gains are achieved while maintaining low computational complexity and fast inference speed, establishing our method as a highly promising and practical solution for next-generation wireless systems.

翻译：深度生成模型通过学习无线信道的复杂先验分布，为传统信道估计提供了强有力的替代方案。基于这一潜力，本文提出了一种基于隐扩散模型的新型信道估计算法，称为用于信道估计的隐扩散后验采样。我们方法的核心是一个专为信道估计设计的轻量级隐扩散模型架构，其作为强大的生成先验来捕捉复杂的信道分布。此外，我们通过引入似然项的有效近似以及在变分自编码器隐空间上定制化的自一致性约束，增强了扩散后验采样过程。大量实验结果表明，该算法在性能上持续优于多种现有方法。值得注意的是，这些显著的性能提升是在保持低计算复杂度和快速推理速度的同时实现的，这确立了我们的方法作为下一代无线系统极具前景且实用的解决方案。