  Channel estimation at millimeter wave (mmWave) is challenging when large
antenna arrays are used. Prior work has leveraged the sparse nature of mmWave
channels via compressed sensing based algorithms for channel estimation. Most
of these algorithms, though, assume perfect synchronization and are vulnerable
to phase errors that arise due to carrier frequency offset (CFO) and phase
noise. Recently sparsity-aware, non-coherent beamforming algorithms that are
robust to phase errors were proposed for narrowband phased array systems with
full resolution analog-to-digital converters (ADCs). Such energy based
algorithms, however, are not robust to heavy quantization at the receiver. In
this paper, we develop a joint CFO and wideband channel estimation algorithm
that is scalable across different mmWave architectures. Our method exploits the
sparsity of mmWave MIMO channel in the angle-delay domain, in addition to
compressibility of the phase error vector. We formulate the joint estimation as
a sparse bilinear optimization problem and then use message passing for
recovery. We also give an efficient implementation of a generalized bilinear
message passing algorithm for the joint estimation in mmWave systems with
one-bit ADCs. Simulation results show that our method is able to recover the
CFO and the channel compressively, even in the presence of phase noise.
