1,701 research outputs found
Channel Estimation for Quantized Systems based on Conditionally Gaussian Latent Models
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 yields a locally Gaussian
channel distribution, thus enabling the application of the well-known Bussgang
decomposition. By exploiting the resulting conditional Bussgang decomposition,
we derive parameterized linear minimum mean square error (MMSE) estimators for
the considered generative latent variable models. In this context, we explore
leveraging model-based structural features to reduce memory and complexity
overhead associated with the proposed estimators. Furthermore, we devise
necessary training adaptations, enabling direct learning of the generative
models from quantized pilot observations without requiring ground-truth channel
samples during the training phase. Through extensive simulations, we
demonstrate the superiority of our introduced estimators over existing
state-of-the-art methods for coarsely quantized systems, as evidenced by
significant improvements in mean square error (MSE) and achievable rate
metrics
- …