  The estimation of millimeter wave (mmWave) massive multiple input multiple
output (MIMO) channels becomes compelling when one-bit analog to digital
converters (ADCs) are utilized. Furthermore, as the number of antenna
increases, pilot overhead scales up to provide consistent channel estimation,
eventually degrading spectral efficiency. This study presents a channel
estimation approach that combines a conditional generative adversarial network
(cGAN) with a novel blind denoising network with a sparse feature attention
mechanism. Performance analysis and simulations show that using a cGAN fused
with a feature attention-based denoising neural network significantly enhances
the channel estimation performance while requiring less pilot transmission.
