IEEE Signal Processing - May 2018 - 52

real network is one of the most critical factors to be consid-
ered. We believe that extensive research work in this direction
is highly desirable.

Data privacy in CS-enabled IoT networks
The data collected from a variety of IoT network sensors,
including data about our daily activities, surroundings, and
even our personal physical information, can be recorded and
analyzed, which at the same time greatly intensifies the risk of
compromising privacy. There are few effective privacy-preserv-
ing mechanisms in mobile sensing systems. To provide privacy
protection, then, some investigators have proposed adding
noise to the original data [75]. The added noise would conceal
the data when the original sparse data points are zero or near
zero, thus reducing the data sparsity. However, CS aims to
achieve this kind of efficiency for sparse data processing. This
creates a conflict between privacy and efficiency involving big
data processing in CS-enabled IoT networks. Therefore, a great
deal of research is expected in this area.

Channel acquisition and precoding in massive MIMO
To satisfy the high data rate requirements stemming from
increasing mobile applications, many efforts have been
made to improve transmission SE. An effective way is to
exploit the spatial degrees of freedom provided by large-
scale antennas at the transmitter and the receiver to form
massive MIMO systems [48]. The work in [48] showed that
the spatial resolution of a large-scale antenna array will be
very high, and the channels corresponding to different users
are approximately orthogonal when the number of antennas
at the BS is very large. Consequently, linear processing is
good enough to make the system performance approach the
optimum if the channel state information (CSI) is known at
the BS.
Accurate CSI at the BS is essential for massive MIMO
to obtain the aforementioned advantage. Because of the large
channel dimension, downlink CSI acquisition in massive
MIMO systems sometimes becomes challenging, even if
uplink CSI estimation is relatively simple. In time-division
duplex (TDD) systems, the downlink CSI can be easily ob-
tained by exploiting channel reciprocity. However, most actu-
ally deployed systems mainly employ frequency-division
duplex (FDD), where channel reciprocity no longer holds.
In this situation, the downlink channel has to be estimat-
ed directly and then fed back to the BS, which results in
extremely high overhead.
To address the CSI estimation and feedback issue in FDD
systems, the sparsity of massive MIMO channels must be ex-
ploited. Researchers have proposed some CS-enabled CSI
acquisition methods for FDD massive MIMO, where they have
successfully exploited the correlation in massive MIMO chan-
nels to reduce the number of training symbols and the amount of
feedback overhead [50].
In this section, we focus on CS-enabled channel acquisition
and its related applications. We will first introduce the channel
sparsity feature, then discuss channel estimation and feedback,
52

and after that explore precoding and detection. We should note
that millimeter-wave (mm-wave) communications are often
used with massive MIMO techniques, since the short wave-
length makes it very easy to pack a large number of antennas in
a small area. In the following discussion, we will also include
channel acquisition and precoding based on CS in mm-wave
massive MIMO, even if mm-wave channels are slightly differ-
ent from the traditional wireless channels.

Sparsity of channels
In the channel acquisition and precoding schemes based on
CS, the key idea is to use the channel sparsity. Although the
channel sparsity in massive MIMO generally exists in the
time domain, the frequency domain, and the spatial domain,
we mainly focus on the spatial-domain channel sparsity in
this section.
In conventional MIMO systems, a rich-scattering mul-
tipath channel model is often assumed, so that the channel
coefficients can be modeled as independent random variables.
However, this assumption is no longer true in massive MIMO
systems. It has been shown that the massive MIMO channel
is spatially correlated and has a sparse structure in the spatial
domain. This correlation and sparsity are due to the exploitation
of high RF and the deployment of large-scale antenna arrays in
future wireless communications. In the high-frequency band,
the channels have fewer propagation paths, while more trans-
mit and receive antennas make the number of distinguished
paths much fewer than the number of channel coefficients; the
rich scatters then become limited or sparse.
As shown in Figure 7, a classical channel model with lim-
ited scatterers at the BS is often used in the literature [49]. In
this model, different user channels have a partially common
sparsity support because of the shared scatterers and have
an independent sparsity support caused by the individual
scatterers in the propagation environments. Using this spar-
sity structure, investigators have proposed many CS-enabled
channel acquisition and precoding schemes, as we will dis-
cuss in the following.

Compressive pilot design
To obtain a good channel estimation, the length of the orthogo-
nal training sequence must be at least the same as the number
of transmit antenna elements. Because of a huge number of
antennas at the BS in massive MIMO systems, the downlink
pilots occupy a high proportion of the resources. Consequently,
the traditional pilot design is not applicable here. It is necessary
to design specific pilots to reduce the training overhead in FDD
massive MIMO systems. Research has shown that channel spa-
tial correlation or sparsity can be used to shrink the original
channel to an effective one with a much lower dimension so
that low-overhead training is enough in massive MIMO sys-
tems. Basing their work on this principle, Lau et al. proposed
CS-enabled pilot design schemes [50].
When exploiting CS theory to acquire the CSI in the corre-
lated FDD MIMO systems, how much training should be sent
is a most important question. Once the amount of the training

IEEE Signal Processing Magazine

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May 2018

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