J.C. ShenJ. ZhangK. LetaiefKWANG-CHENG CHEN2018-09-102018-09-102017-03http://scholars.lib.ntu.edu.tw/handle/123456789/400211Massive multiple-input-multiple-output (MIMO) has been regarded as one of the key technologies for fifth-generation wireless networks, as it can significantly improve both the spectral efficiency and the energy efficiency. The availability of high-dimensional channel side information (CSI) is critical for its promised performance gains, but the overhead of acquiring CSI may potentially deplete the available radio resources. Fortunately, it has recently been discovered that harnessing various sparsity structures in massive MIMO channels can lead to significant overhead reduction, and thus improve the system performance. This paper presents and discusses the use of sparsity-inspired CSI acquisition techniques for massive MIMO, as well as the underlying mathematical theory. Sparsity-inspired approaches for both frequency-division duplexing and time-division duplexing massive MIMO systems will be examined and compared from an overall system perspective, including the design tradeoffs between the two duplexing modes, computational complexity of acquisition algorithms, and applicability of sparsity structures. Meanwhile, some future prospects for research on high-dimensional CSI acquisition to meet practical demands will be identified. © 2017 IEEE.Channel estimation; compressed sensing; massive multiple-input-multiple-output (MIMO); pilot contamination; pilot sequences; sparsity; ℓ1 minimization[SDGs]SDG7Channel state information; Complex networks; Computational complexity; Energy efficiency; Mergers and acquisitions; MIMO systems; Channel side information; Frequency division duplexing; Mathematical theory; Overhead reductions; Performance Gain; Sparsity structure; Spectral efficiencies; Time division duplexing; Information retrievaljournal article10.1109/JSYST.2015.24486612-s2.0-84997848516WOS:000397779700005