  Massive multiple-input multiple-output (MIMO) is a promising approach for
cellular communication due to its energy efficiency and high achievable data
rate. These advantages, however, can be realized only when channel state
information (CSI) is available at the transmitter. Since there are many
antennas, CSI is too large to feed back without compression. To compress CSI,
prior work has applied compressive sensing (CS) techniques and the fact that
CSI can be sparsified. The adopted sparsifying bases fail, however, to reflect
the spatial correlation and channel conditions or to be feasible in practice.
In this paper, we propose a new sparsifying basis that reflects the long-term
characteristics of the channel, and needs no change as long as the spatial
correlation model does not change. We propose a new reconstruction algorithm
for CS, and also suggest dimensionality reduction as a compression method. To
feed back compressed CSI in practice, we propose a new codebook for the
compressed channel quantization assuming no other-cell interference. Numerical
results confirm that the proposed channel feedback mechanisms show better
performance in point-to-point (single-user) and point-to-multi-point
(multi-user) scenarios.
