Signal Processing - July 2016 - 11

Zhang of the University of Wisconsin-
Madison, plan to demonstrate the feasibility of a large-scale millimeter-wave
wireless data network that can operate
at gigabit speeds. Their goal is to develop a technology using concepts that
span signal processing, network protocols, circuit design, and communication
architecture. "The highly directional
nature of millimeter-wave links requires
rethinking wireless network protocols
based on omnidirectional transmission,
such as the 'listen before talk' protocol
underlying Wi-Fi," Madhow says.
"Signal processing is at the heart of
both the communications and radar
research we are pursuing," Madhow
says. The hardware characteristics necessary for millimeter-wave systems are
quite different from those in existing
wireless systems, which calls for the
invention of new signal processing techniques. "For example, the small wavelengths make it possible to realize
antenna arrays with a very large number
of elements in a small form factor-a
1,000-element antenna array could fit
within the palm of the hand-which
makes it possible to synthesize narrow,
electronically steerable beams."
Such arrays typically use radio-frequency (RF) beamforming that allows
control over the phases-and, in some
cases, the amplitudes-of each array
element but does not allow access to the
signals transmitted or received by the
individual elements. "This means that
standard adaptive signal processing
techniques, typically based on least
squares, cannot be applied to train such
arrays," Madhow says. "We have therefore had to invent new compressive
techniques for this purpose."
The biggest signal processing challenge facing millimeter-wave communication and radar system developers is
the fact that classical algorithms and
models often do not apply to the technology, due to its tiny wavelengths and
associated hardware constraints. "Thus,
while we can leverage many core concepts, we have had to invent new
approaches both in terms of modeling
and algorithms," Madhow says.
To allow millimeter-wave signals to
work with relatively large and complex

"mostly analog" processing architecture
antenna arrays, the researchers develfor a line-of-sight (LoS), multiple-input,
oped a compressive estimation approach.
multiple-output (MIMO) prototype, and
The technique exploits the sparsity of the
an analog multiband approach for dividmillimeter-wave channel to estimate, via
ing the communication bandwidth into
randomized measurements, the direcsmaller slices that can be efficiently digtions of the dominant arrival/departure
itized for processing.
paths between the transmitter and receiv"Modern communication transceiver
er. The approach falls into the general
design is heavily based on digital signal
area of sparse modeling and compressive
processing (DSP) and can therefore exsensing, Madhow says. "However, stanploit the economies of scale from
dard compressive sensing algorithms do
Moore's law," Madhow observes. "This
not apply to the settings of interest to us,
approach is predisince they are based
cate d on a na logon sparsity in a disto-digital converters
crete setting, while
"signal processing
(ADCs) that can faiththe parameters we
is at the heart of both
fully represent sigwish to estimate are
the communications
nals in the digital
continuous-valued."
and radar research
domain," he says. "As
Although a subwe are pursuing."
we increase the comstantial amount of
munication bandtheory already exists
width, however, ADCs
in the area of combecome costly and power-hungry, hence
pressive sensing, the researchers still
we have been exploring various alternaneeded to develop a general theory of
tives that trade off the complexity of anacompressive estimation. "Over the past
log and DSP."
few years, we have developed both theories and algorithms in this area,"
Madhow says. "Our goal now is to
Biomimetic antenna arrays
experimentally demonstrate these
Engineers at the University of Akron's
using a millimeter-wave test bed and to
College of Engineering's Wireless Comdesign network architectures and promunications Lab are developing electritocols around these core signal procally small biomimetic antenna arrays
cessing ideas."
inspired by one of the most sensitive
Other areas of millimeter-wave
auditory systems in the natural world: an
research also required fresh approachinsect's ear system. In collaboration with
es. For short-range millimeter-wave
the University of Wisconsin-Madison,
imaging-used in applications such as
researchers are investigating methods
gesture recognition and vehicular situaaimed at increasing the data rate in
tional awareness-the researchers realMIMO wireless communication sysized that signal processing must be
tems while also reducing the size and
based on models that go beyond the
power consumption of associated
classic point scatterer target model,
mobile devices.
since targets appear larger at short
"The overall objective of this interranges and small wavelengths. "We are
disciplinary research project is to use
therefore pursuing new patch-based tarrecent advances in the areas of multianget models optimized using tools from
tenna wireless communications, signal
estimation theory," Madhow says. "We
processing, and coupled antenna array
are employing both classical correla(CAA) technology to enhance the effition-based signal processing and sparse
ciency of spectrum utilization of mobile
signal processing techniques based on
wireless communication systems," says
these models."
Hamid Bahrami, the project's principal
For handling large communication
investigator and an associate professor
bandwidths, the researchers have
in the University of Akron's Department
explored several different options, tradof Electrical and Computer Engineering.
ing off both analog and digital signal
The project's coprincipal investigator is
processing. Two recent examples are a
Nader Behdad at the University of
IEEE Signal Processing Magazine

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July 2016

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Table of Contents for the Digital Edition of Signal Processing - July 2016

Signal Processing - July 2016 - Cover1
Signal Processing - July 2016 - Cover2
Signal Processing - July 2016 - 1
Signal Processing - July 2016 - 2
Signal Processing - July 2016 - 3
Signal Processing - July 2016 - 4
Signal Processing - July 2016 - 5
Signal Processing - July 2016 - 6
Signal Processing - July 2016 - 7
Signal Processing - July 2016 - 8
Signal Processing - July 2016 - 9
Signal Processing - July 2016 - 10
Signal Processing - July 2016 - 11
Signal Processing - July 2016 - 12
Signal Processing - July 2016 - 13
Signal Processing - July 2016 - 14
Signal Processing - July 2016 - 15
Signal Processing - July 2016 - 16
Signal Processing - July 2016 - 17
Signal Processing - July 2016 - 18
Signal Processing - July 2016 - 19
Signal Processing - July 2016 - 20
Signal Processing - July 2016 - 21
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Signal Processing - July 2016 - 23
Signal Processing - July 2016 - 24
Signal Processing - July 2016 - 25
Signal Processing - July 2016 - 26
Signal Processing - July 2016 - 27
Signal Processing - July 2016 - 28
Signal Processing - July 2016 - 29
Signal Processing - July 2016 - 30
Signal Processing - July 2016 - 31
Signal Processing - July 2016 - 32
Signal Processing - July 2016 - 33
Signal Processing - July 2016 - 34
Signal Processing - July 2016 - 35
Signal Processing - July 2016 - 36
Signal Processing - July 2016 - 37
Signal Processing - July 2016 - 38
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Signal Processing - July 2016 - 40
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Signal Processing - July 2016 - 42
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Signal Processing - July 2016 - 47
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Signal Processing - July 2016 - 88
Signal Processing - July 2016 - 89
Signal Processing - July 2016 - 90
Signal Processing - July 2016 - 91
Signal Processing - July 2016 - 92
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Signal Processing - July 2016 - 94
Signal Processing - July 2016 - 95
Signal Processing - July 2016 - 96
Signal Processing - July 2016 - 97
Signal Processing - July 2016 - 98
Signal Processing - July 2016 - 99
Signal Processing - July 2016 - 100
Signal Processing - July 2016 - 101
Signal Processing - July 2016 - 102
Signal Processing - July 2016 - 103
Signal Processing - July 2016 - 104
Signal Processing - July 2016 - Cover3
Signal Processing - July 2016 - Cover4
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