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

In mmWave massive multiple-input multiple-output (mMIMO) systems, hybrid (digital/analog) structure has been a prevalent option to balance system cost and performance. To facilitate transceiver design in hybrid mmWave mMIMO, acquiring an accurate channel state information is critical. 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-utilized beamspace sparsity alongside 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 mmWave channel estimator not only works in doubly-selective channels, but also largely reduces the training overhead, storage demand as well as computational complexity.