  The beam squint effect, which manifests in different steering matrices in
different sub-bands, has been widely considered a challenge in millimeter wave
(mmWave) multiinput multi-output (MIMO) channel estimation. Existing methods
either require specific forms of the precoding/combining matrix, which restrict
their general practicality, or simply ignore the beam squint effect by only
making use of a single sub-band for channel estimation. Recognizing that
different steering matrices are coupled by the same set of unknown channel
parameters, this paper proposes to exploit the common sparsity structure of the
virtual channel model so that signals from different subbands can be jointly
utilized to enhance the performance of channel estimation. A probabilistic
model is built to induce the common sparsity in the spatial domain, and the
first-order Taylor expansion is adopted to get rid of the grid mismatch in the
dictionaries. To learn the model parameters, a variational
expectation-maximization (EM) algorithm is derived, which automatically obtains
the balance between the likelihood function and the common sparsity prior
information, and is applicable to arbitrary forms of precoding/combining
matrices. Simulation results show the superior estimation accuracy of the
proposed algorithm over existing methods under different noise powers and
system configurations.
