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Generative Diffusion-Based Bayesian Modeling for Universal Channel Estimation
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DOI：10.1109/JSAC.2025.3648954.png)
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Abstract
En 中文
The growth of frequency bandwidths in the new generation of wireless networks gives rise to the multitude of wireless communication scenarios and highlights the challenges of generalized capability of the wireless communication system in different scenarios, especially the channel estimation module. In this paper, we propose a large model dubbed Conditional Latent Diffusion Channel Generation Model (C-LCGM) to learn the distributions of channel state information (CSI) in different wireless communication scenarios to form Bayesian Modeling based Channel Estimation Scheme (BMCE) for universal channel estimation. BMCE conducts universal channel estimation by generating reference CSIs from C-LCGM and mapping the reference CSIs to the optimal channel estimation neural network for each scenario. Specifically in C-LCGM, we propose to compress the CSIs into latent codes and design a conditional diffusion model to model the distribution of the latent codes given the large-scale parameters (LSP) of CSIs as the condition. Further in BMCE, we propose to deploy C-LCGM on the server center and design a hyper-network dubbed Parameters Generating Module (PGM) to map the generated CSIs of C-LCGM to the channel estimation networks for the base stations (BS) according to the reported LSPs. The design rationale and training loss of C-LCGM and BMCE are derived theoretically in this paper. We also conduct extensive simulations to verify the performance of C-LCGM and BMCE. The simulation results show that BMCE can achieve optimal channel estimation performance in different and novel scenarios with C-LCGM generating high-quality CSIs approximating the real CSIs in each scenario. Complexity analysis shows BMCE can fit the delay requirement of wireless communication systems.
Keywords:
Channel estimation
wireless communication environment
diffusion model
hyper-network
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