Enhancing Channel Estimation in Quantized Systems with a Generative Prior

• URL: http://arxiv.org/abs/2405.03542v1
• Date: Fri, 26 Apr 2024 09:27:59 GMT
• Title: Enhancing Channel Estimation in Quantized Systems with a Generative Prior
• Authors: Benedikt Fesl, Aziz Banna, Wolfgang Utschick,
• Abstract summary: We propose to leverage a Gaussian mixture model (GMM) as generative prior, capturing the channel distribution of the propagation environment. The proposed estimation framework exhibits adaptability to higher resolution systems and alternative generative priors.
• Score: 9.486021754040483
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Channel estimation in quantized systems is challenging, particularly in low-resolution systems. In this work, we propose to leverage a Gaussian mixture model (GMM) as generative prior, capturing the channel distribution of the propagation environment, to enhance a classical estimation technique based on the expectation-maximization (EM) algorithm for one-bit quantization. Thereby, a maximum a posteriori (MAP) estimate of the most responsible mixture component is inferred for a quantized received signal, which is subsequently utilized in the EM algorithm as side information. Numerical results demonstrate the significant performance improvement of our proposed approach over both a simplistic Gaussian prior and current state-of-the-art channel estimators. Furthermore, the proposed estimation framework exhibits adaptability to higher resolution systems and alternative generative priors.

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