Diffusion-based Generative Prior for Low-Complexity MIMO Channel
  Estimation
Benedikt Fesl Michael Baur Florian Strasser Michael Joham Wolfgang Utschick
This work proposes a novel channel estimator based on diffusion models (DMs),
one of the currently top-rated generative models. Contrary to related works
utilizing generative priors, a lightweight convolutional neural network (CNN)
with positional embedding of the signal-to-noise ratio (SNR) information is
designed by learning the channel distribution in the sparse angular domain.
Combined with an estimation strategy that avoids stochastic resampling and
truncates reverse diffusion steps that account for lower SNR than the given
pilot observation, the resulting DM estimator has both low complexity and
memory overhead. Numerical results exhibit better performance than
state-of-the-art channel estimators utilizing generative priors.