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MIMO Channel Estimation Using Transformer-Based Generative Diffusion Models
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AI
DOI：10.1109/tvt.2026.3680259.png)
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Abstract
En 中文
Recent researches indicate that artificial intelligence (AI) holds significant potential for channel estimation. However, most existing AI-based channel estimators rely on training channel samples, leading to reduced estimation accuracy on out-of-distribution data. To address this problem, we propose a robust channel estimator for multiple-input multiple-output (MIMO) system called Transformer-based diffusion model (TDM), which is trained under an unsupervised process and independent of channel distribution. We perform the Transformer structure to accurately denoise from noisy channel matrix. A posterior sampling is developed at inference time to channel reconstruction. Experimental results demonstrate the superiority and out-of-distribution robustness of the TDM scheme.
Keywords:
MIMO channel estimation
generative AI
diffusion models
attention mechanism
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