IEEE Signal Processing - May 2018 - 46
In the following, we discuss three major applications of CS in
5G and IoT networks.
CS-enabled CRNs
r (t )
Compressive
Sampling
x = Φ r (t )
Recent developments in CS theory inspire sub-Nyquist sam-
pling by utilizing the sparse nature of signals [3]. Driven by
the inborn nature of the sparsity property of signals in wire-
less communications, e.g., the sparse utilization of spectrum,
CS theory is capable of enabling sub-Nyquist sampling for
wide-band spectrum sensing.
Energy detection-based compressive spectrum sensing
The work in [28] applied CS theory to wide-band spectrum
sensing, achieving sub-Nyquist sampling without any loss of
information. In Figure 4, we summarize a general framework
for compressive spectrum sensing with the energy detection
method, where the analog signal at the receiver r ^ t h has a
sparse representation s f in the frequency domain. The received
Sf
Spectrum
Recovery
Energy
Detection
rf
"
Θ = Φ F -1
x
Standard compressive spectrum sensing
"
In this section, we introduce the applications of CS in CRNs.
Radio-frequency (RF) spectrum is a valuable but tightly regulat-
ed resource, because of its unique and important role in wireless
communications. The demand for RF spectrum is increasing
because of a rapidly expanding market for multimedia wireless
services, while the usable spectrum is becoming scarce because
of the current rigid spectrum allocation policies. However,
according to reports from the Federal Communications
Commission and the U.K.'s Office of Communications, more
commonly known as Ofcom, localized temporal and geographic
spectrum utilization is, in reality, extremely low and unbal-
anced. CR has become a promising approach to solving the
spectrum scarcity problem, by allowing unlicensed secondary
users (SUs) to opportunistically access a licensed band when
the licensed primary user (PU) is absent. To avoid any harmful
interference with the PUs, SUs in CRNs should be aware of the
occupancy of the spectrum of interest.
Spectrum sensing, which detects the spectrum holes (the fre-
quency bands that are not utilized by PUs), is one of the most
challenging tasks in CR. As the radio environment changes over
time and space, an efficient spectrum-sensing technique should
be capable of tracking these fast changes [26]. A good approach
for detecting PUs is to adopt the traditional narrowband sensing
algorithms, which include energy detection, matched filtering,
and cyclostationary feature detection. Here, the term narrowband implies that the frequency range is sufficiently narrow that
the channel frequency response can be considered flat. In other
words, the bandwidth of interest is less than the coherence band-
width of the channel [27].
While the present spectrum-sensing algorithms have
focused on exploiting spectral opportunities over a narrow
frequency band, future CRNs will eventually be required to
exploit spectral opportunities over a wide frequency range,
from hundreds of megahertz to several gigahertz, to improve
SE and achieve higher opportunistic throughput. As driven by
Nyquist-Shannon sampling theory, a simple approach is to
acquire the wide-band signal directly by a high-speed ADC,
which is particularly challenging or even unaffordable, espe-
cially for energy-constrained devices, such as smartphones or
even battery-free devices. Therefore, revolutionary wide-band
spectrum-sensing techniques are greatly desired to relieve the
burden on high-speed ADCs.
min WSf W
lp
P×1
+
2
P×N
K Nonzero Entries
K
Table of Contents for the Digital Edition of IEEE Signal Processing - May 2018
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