  In mmWave massive multiple-input multiple-output (mMIMO) systems, hybrid
digital/analog beamforming has been recognized as an economic means to overcome
the severe mmWave propagation loss. To facilitate beamforming for mmWace mMIMO,
there is a great urgency to acquire accurate channel state information. To this
end, a novel doubly-sparse approach is proposed to estimate doubly-selective
mmWave channels under hybrid mMIMO. Via the judiciously designed training
pattern, the well-known beamspace sparsity along with the under-investigated
delay-domain sparsity that mmWave channels exhibit can be jointly exploited to
assist channel estimation. Thanks to our careful two-stage (random-probing and
steering-probing) design, the proposed channel estimator possesses strong
robustness against the double (frequency and time) selectivity whilst enjoying
the benefits brought by the exploitation of double sparsity. Compared with
existing alternatives, our proposed channel estimator not only proves to be
more general, but also largely reduces the training overhead, storage demand as
well as computational complexity.
