Enhancing Channel Estimation in Quantized Systems with a Generative Prior
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866907892171407360 |
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| author | Fesl, Benedikt Banna, Aziz Utschick, Wolfgang |
| author_facet | Fesl, Benedikt Banna, Aziz Utschick, Wolfgang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_03542 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Channel Estimation in Quantized Systems with a Generative Prior Fesl, Benedikt Banna, Aziz Utschick, Wolfgang Signal Processing Information Theory Machine Learning 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. |
| title | Enhancing Channel Estimation in Quantized Systems with a Generative Prior |
| topic | Signal Processing Information Theory Machine Learning |
| url | https://arxiv.org/abs/2405.03542 |