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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.21386 |
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| _version_ | 1866909867705368576 |
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| author | Fan, Xiaotian Zhou, Xingyu Liang, Le Jin, Shi |
| author_facet | Fan, Xiaotian Zhou, Xingyu Liang, Le Jin, Shi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_21386 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Low-Complexity MIMO Channel Estimation with Latent Diffusion Models Fan, Xiaotian Zhou, Xingyu Liang, Le Jin, Shi Information Theory 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. |
| title | Low-Complexity MIMO Channel Estimation with Latent Diffusion Models |
| topic | Information Theory |
| url | https://arxiv.org/abs/2510.21386 |