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Low-Complexity MIMO Channel Estimation with Latent Diffusion Models

Xiaotian Fan, Xingyu Zhou, Le Liang, Shi Jin

TL;DR

This work tackles the challenge of high-dimensional MIMO channel estimation under limited pilot resources by learning a generative prior over channels with latent diffusion models. It proposes PSLD-CE, a lightweight framework that performs diffusion in a VAE latent space, combining a pre-trained prior with a tractable likelihood via a denoiser-guided update and a self-consistency regularizer to stabilize posterior sampling. The method achieves superior NMSE performance while maintaining a small model size and fast inference, demonstrated on 3GPP CDL channels with Nt=64 and Nr=16 and significantly lower FLOPs than competing diffusion-based approaches. Overall, PSLD-CE provides a practical, high-performance channel estimator suitable for next-generation wireless systems with constrained pilot resources.

Abstract

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.

Low-Complexity MIMO Channel Estimation with Latent Diffusion Models

TL;DR

This work tackles the challenge of high-dimensional MIMO channel estimation under limited pilot resources by learning a generative prior over channels with latent diffusion models. It proposes PSLD-CE, a lightweight framework that performs diffusion in a VAE latent space, combining a pre-trained prior with a tractable likelihood via a denoiser-guided update and a self-consistency regularizer to stabilize posterior sampling. The method achieves superior NMSE performance while maintaining a small model size and fast inference, demonstrated on 3GPP CDL channels with Nt=64 and Nr=16 and significantly lower FLOPs than competing diffusion-based approaches. Overall, PSLD-CE provides a practical, high-performance channel estimator suitable for next-generation wireless systems with constrained pilot resources.

Abstract

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.
Paper Structure (15 sections, 11 equations, 4 figures, 2 tables, 1 algorithm)

This paper contains 15 sections, 11 equations, 4 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Preliminaries
  3. MIMO Channel Estimation
  4. Latent Diffusion Models
  5. Proposed Methods
  6. VAE Architecture for Latent Channel Compression
  7. Network Architecture
  8. Key Design Considerations
  9. Latent Space Diffusion for Channel Estimation
  10. Prior score
  11. Likelihood score
  12. Simulation Results
  13. Selecting the Latent Dimension
  14. Channel Estimation
  15. Conclusion

Figures (4)

  • Figure 1: LDM architecture with a lightweight VAE and a time-embedded CNN for the DM.
  • Figure 2: NMSE of channel estimation with respect to different latent dimensions of VAE.
  • Figure 3: The NMSE versus the SNR with QPSK pilot when $N_p/N_t = 0.6$.
  • Figure 4: The NMSE versus the SNR with DFT pilot when $N_p/N_t = 1$.