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.
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Manuscript received October 2, 2019; revised March 11, 2020; accepted May 10, 2020. Date of publication May 27, 2020; date of current version September 10, 2020. This work was supported in part by the Key-Area Research and Development Program of Guangdong Province under Project 2019B010153003, and in part by the National Science Foundation under Grant CPS-1932413 and Grant ECCS-1935915. This article was presented at the IEEE International Conference on Communications, Shanghai, China, May 20-24, 2019. The associate editor coordinating the review of this article and approving it for publication was X. Yuan. (Corresponding author: Xiang Cheng.) Shijian Gao and Liuqing Yang are with the Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523-1373 USA (e-mail: email@example.com; firstname.lastname@example.org).
- channel estimation
- double selectivity
- double sparsity
- hybrid massive multiple-input multiple-output