Low-Complexity MIMO Channel Estimation with Latent Diffusion Models

TL;DR

This paper introduces PSLD-CE, a low-complexity channel estimation method using latent diffusion models that effectively captures complex wireless channel distributions, outperforming existing techniques in accuracy and speed.

Contribution

The paper presents a novel lightweight latent diffusion model architecture for channel estimation, with improved likelihood approximation and self-consistency constraints, enhancing performance and efficiency.

Findings

Outperforms existing channel estimation methods in accuracy.

Maintains low computational complexity and fast inference.

Demonstrates significant performance gains in experimental results.

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.