Channel Estimation for Quantized Systems Based on Conditionally Gaussian Latent Models
Benedikt Fesl,
Nurettin Turan,
Benedikt Böck
et al.
Abstract:This work introduces a novel class of channel estimators tailored for coarse quantization systems. The proposed estimators are founded on conditionally Gaussian latent generative models, specifically Gaussian mixture models (GMMs), mixture of factor analyzers (MFAs), and variational autoencoders (VAEs). These models effectively learn the unknown channel distribution inherent in radio propagation scenarios, providing valuable prior information. Conditioning on the latent variable of these generative models yiel… Show more
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