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In mmWave massive multiple-input multiple-output (mMIMO) systems, hybriddigital/analog beamforming has been recognized as an economic means to overcomethe severe mmWave propagation loss. To facilitate beamforming for mmWace mMIMO,there is a great urgency to acquire accurate channel state information. To thisend, a novel doubly-sparse approach is proposed to estimate doubly-selectivemmWave channels under hybrid mMIMO. Via the judiciously designed trainingpattern, the well-known beamspace sparsity along with the under-investigateddelay-domain sparsity that mmWave channels exhibit can be jointly exploited toassist channel estimation. Thanks to our careful two-stage (random-probing andsteering-probing) design, the proposed channel estimator possesses strongrobustness against the double (frequency and time) selectivity whilst enjoyingthe benefits brought by the exploitation of double sparsity. Compared withexisting alternatives, our proposed channel estimator not only proves to bemore general, but also largely reduces the training overhead, storage demand aswell as computational complexity.

Estimating Doubly-Selective Channels for Hybrid mmWave Massive MIMO Systems: A Doubly-Sparse Approach