IEEE Signal Processing - May 2018 - 54

Zero Entry
Nonzero Entry

Support from
Prior Signals

Support from
Current Signals
(a)

Measurement Matrix
Φ ∈ NM × L
Sparse Basis of Channel
L×G

Sparse Representation
h = Ψg

Estimate g Based on
Pursuit Algorithms

Channel Reconstruction
h = Ψg
"

Received Signals
y = ΦΨg + w

"

Ψ∈

Received Signals
Corresponding to N
Received Symbols
y = r0T . . . rLT

T

∈

NM × 1

(b)

FIGURE 8. An illustration of compressive channel estimation with time-domain correlation signal support: (a) support from prior and current signals and
(b) compressive channel estimation by using time-domain correlation.

Rao and Lau developed a greedy pursuit-based approach with
the prior support information and its quality information [8],
where the prior support information is adaptively exploited
based on its quality information to further improve the chan-
nel estimation performance.
Besides channel estimation, [55] and [49] also proposed
several channel feedback schemes. To exploit the common sup-
port, Shen et al. developed a CS-enabled differential CSI feed-
back scheme [55] by using the temporal correlation of MIMO
channels. The proposed scheme can reduce the feedback
overhead by about 20% compared with the direct CS-enabled
channel feedback. In [49], Liu et al. developed a robust closed-
loop pilot and CSI at the transmitter (CSIT) feedback resource
adaptation framework by using the temporal correlation of
multiuser massive MIMO channels. In this framework, the
pilot and feedback resources can be adaptively adjusted for
54

successful CSIT recovery under unknown and time-varying
channel sparsity levels.

With spatial-domain sparsity
In practice, the antenna spacing in massive MIMO is usually
set to be half-wave length to keep the array aperture compact.
Furthermore, the BS with a large-scale antenna array is gener-
ally deployed at the top of high buildings, such that there are
only limited local scatters [57]. In this case, the large-scale
MIMO channels exhibit strong angular-domain sparsity or
spatial sparsity. This channel sparsity property can be used to
reduce the channel estimation and feedback overhead in FDD
massive MIMO systems.
In [58], Tseng et al. employed a block optimization algo-
rithm to extract the common angular support information
from the channel matrices. The extracted common support

IEEE Signal Processing Magazine

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May 2018

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Table of Contents for the Digital Edition of IEEE Signal Processing - May 2018

Contents
IEEE Signal Processing - May 2018 - Cover1
IEEE Signal Processing - May 2018 - Cover2
IEEE Signal Processing - May 2018 - Contents
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IEEE Signal Processing - May 2018 - Cover3
IEEE Signal Processing - May 2018 - Cover4
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