  We study the maximum likelihood problem for the blind estimation of massive
mmWave MIMO channels while taking into account their underlying sparse
structure, the temporal shifts across antennas in the broadband regime, and
ultimately one-bit quantization at the receiver. The sparsity in the angular
domain is exploited as a key property to enable the unambiguous blind
separation between user's channels. The main advantage of this approach is the
fact that the overhead due to pilot sequences can be dramatically reduced
especially when operating at low SNR per antenna. In addition, as sparsity is
the only assumption made about the channel, the proposed method is robust with
respect to the statistical properties of the channel and data and allows the
channel estimation and the separation of interfering users from adjacent base
stations to be performed in rapidly time-varying scenarios. For the case of
one-bit receivers, a blind channel estimation is proposed that relies on the
Expectation Maximization (EM) algorithm. Additionally, performance limits are
derived based on the clairvoyant Cramer Rao lower bound. Simulation results
demonstrate that this maximum likelihood formulation yields superior estimation
accuracy in the narrowband as well as the wideband regime with reasonable
computational complexity and limited model assumptions.
