  We propose sparsity-adaptive beamspace channel estimation algorithms that
improve accuracy for 1-bit data converters in all-digital millimeter-wave
(mmWave) massive multiple-input multiple-output (MIMO) basestations. Our
algorithms include a tuning stage based on Stein's unbiased risk estimate
(SURE) that automatically selects optimal denoising parameters depending on the
instantaneous channel conditions. Simulation results with line-of-sight (LoS)
and non-LoS mmWave massive MIMO channel models show that our algorithms improve
channel estimation accuracy with 1-bit measurements in a
computationally-efficient manner.
