  Massive MIMO has been regarded as one of the key technologies for 5G wireless
networks, as it can significantly improve both the spectral efficiency and
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 trade-offs 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.
