Enhancing Channel Estimation in Quantized Systems with a Generative Prior
<|reference_start|>Enhancing Channel Estimation in Quantized Systems with a Generative Prior: 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.<|reference_end|>
@article{fesl2024enhancing,
    title={Enhancing Channel Estimation in Quantized Systems with a Generative
  Prior},
    author={Benedikt Fesl and Aziz Banna and Wolfgang Utschick},
    journal={arXiv preprint arXiv:2405.03542},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03542},
    primaryClass={eess.SP cs.IT cs.LG math.IT}
}