  Millimeter-wave (mmWave) communication in combination with massive multiuser
multiple-input multiple-output (MU-MIMO) enables high-bandwidth data
transmission to multiple users in the same time-frequency resource. The strong
path loss of wave propagation at such high frequencies necessitates accurate
channel state information to ensure reliable data transmission. We propose a
novel channel estimation algorithm called BEAmspace CHannel EStimation
(BEACHES), which leverages the fact that wave propagation at mmWave frequencies
is predominantly directional. BEACHES adaptively denoises the channel vectors
in the beamspace domain using an adaptive shrinkage procedure that relies on
Stein's unbiased risk estimator (SURE). Simulation results for line-of-sight
(LoS) and non-LoS mmWave channels reveal that BEACHES performs on par with
state-of-the-art channel estimation methods while requiring orders-of-magnitude
lower complexity. To demonstrate the effectiveness of BEACHES in practice, we
develop a very large-scale integration (VLSI) architecture and provide
field-programmable gate array (FPGA) implementation results. Our results show
that adaptive channel denoising can be performed at high throughput and in a
hardware-friendly manner for massive MU-MIMO mmWave systems with hundreds of
antennas.
