Signal Processing - November 2017 - 12
is rapidly emerging. The key enablers of
the most recent deployed generation of
mobile networks, the so-called fourth
generation (4G), have been the develop-
ment of methods to exploit the spatial di-
versity afforded by the wireless medium
in the forms of multiple-input, multiple
output (MIMO) antenna systems, coop-
eration, and relaying; the exploitation of
frequency diversity through the use of
orthogonal frequency-division multiple
access signaling; and the development of
methods to approach link capacity via
the iterative decoding of turbo or lowdensity parity-check codes. These signal
processing advances have allowed 4G
networks to meet the challenge of realtime multimedia communications that
has been the primary advance of 4G over
its predecessors.
The emerging generation of mobile
networks, 5G, presents a number of new
signal processing challenges. Beyond
providing adequate capacity and reli-
ability, 5G networks also add the issue
of energy efficiency, required to support
several new applications areas. These
include the so-called Internet of Things
(IoT), which is envisioned to involve
orders-of-magnitude more terminals
than 4G networks in highly densified
configurations of low-complexity ter-
minals; systems requiring autonomy or
telecontrol, in which low latency and
very high reliability are critical; and
immersive experiences, such as virtual
reality, which require very high band-
width streaming [7].
These requirements give rise to a
number of open problems and potential
solutions. Solutions enabling densifi-
cation and the consequent interference
management include cloud radio access
networks, massive MIMO systems, mil-
limeter wave techniques, and transceivers
that can harvest radio-frequency energy
from their surroundings. Substantial ca-
pacity enhancements are also needed,
and some techniques for providing greater
capacity (in addition to densification of re-
sources) include full duplex transmission
and nonorthogonal multiple-access tech-
niques, both of which will be enabled by
sophisticated signal processing. Security
is another issue in which signal process-
ing has a key role to play; traditionally,
12
security has been a higher-layer issue, with
encryption being a primary mechanism.
However, with highly dense networks of
low-complexity terminals connected via
loosely organized networks, new meth-
ods are needed. Physical layer security
is such a promising method, which relies
on signal processing techniques, such as
coding, beamforming, and signal design.
Finally, many emerging applications,
such as autonomous vehicles and factory
automation, require low-latency, high-reli-
ability communications via short packets.
Since the existing theory of reliable data
transmission is largely based on analyses
in the asymptote of infinite block-length,
new theories are needed to understand
the limits of reliable communication in
this regime. In addition, in applications
such as autonomous driving, worst-case
metrics may be more appropriate than the
standard average-case analysis.
Open problems in speech
and language processing
Spoken language processing encompasses
methods and techniques for transforming
and manipulating speech, text, and a wide
variety of related symbolic representa-
tions. Examples are speech recognition
(speech"words), natural language un-
derstanding (words"meaning), natural
language generation (meaning"words),
speech synthesis (words"speech), and
machine translation (words in L1"words
in L2). Modern applications of spoken
language processing will typically incor-
porate many if not all of these component
technologies [8]-[10]. For example, intelli-
gent agents such as Siri and Alexa require
all of the aforementioned technologies to
support conversations over a wide range
of topics in many languages.
Since virtually all spoken language
processing involves classification and/or
prediction, modern approaches depend
heavily on statistical models and ma-
chine learning. A major breakthrough
in recent years has been the widespread
deployment of deep learning [9]. The
ability of neural networks to automati-
cally learn low-level features, the use of
attention mechanisms to learn which fea-
tures are important, and the flexibility to
scale parameter sets both in width and
depth has led to significant performance
IEEE SIGNAL PROCESSING MAGAZINE
|
November 2017
|
improvements. For example, word er-
ror rates for real-time large vocabulary
speaker-independent speech recogni-
tion are now routinely below 10%, and
speech synthesis quality is acceptable
for most applications.
The renaissance of neural networks
has also been the catalyst for the devel-
opment of a powerful toolbox of core
network components (such as deep neu-
ral networks, long short-term memory
networks, convolutional neural networks,
and more) and development tools (such as
TensorFlow, Torch, and others), which al-
low solutions to complex problems to be
assembled, trained, and deployed quickly
and at a relatively low cost.
Despite the undoubted progress wit-
nessed over the last decade, there re-
main many challenges. The recognition
of fluent conversations between human
speakers and speech in high levels of
background noise or in the presence of a
competing talker still falls well short of
human performance. Our ability to un-
derstand the meaning of natural language
sentences, especially in the context of past
interactions and a changing real-world
environment, remains extremely limited.
Two emerging trends aimed at ad-
dressing some of the challenges are con-
tinuous representations and end-to-end
training. In particular, there is currently a
shift away from symbolic representations
to continuous space representations. An
already well-established example of this is
the use of word embeddings. By project-
ing discrete words into a continuous highdimensional space, many of the problems
associated with synonyms, antonyms,
and rare words are mitigated by the use
of simple well-behaved distance metrics.
The extension of embeddings to represent
whole sentences and conversations enables
variable-length sequences to be mapped
into fixed-length vectors that can then be
manipulated using conventional clas-
sification and prediction models. There is
also increasing emphasis on end-to-end
training. Conventional systems are typi-
cally built as a pipeline of processes for
which each component interface needs
to be explicitly defined, and training data
needs to be appropriately labeled at every
component interface. This is expensive and
inevitably results in information loss as the
Table of Contents for the Digital Edition of Signal Processing - November 2017
Signal Processing - November 2017 - Cover1
Signal Processing - November 2017 - Cover2
Signal Processing - November 2017 - 1
Signal Processing - November 2017 - 2
Signal Processing - November 2017 - 3
Signal Processing - November 2017 - 4
Signal Processing - November 2017 - 5
Signal Processing - November 2017 - 6
Signal Processing - November 2017 - 7
Signal Processing - November 2017 - 8
Signal Processing - November 2017 - 9
Signal Processing - November 2017 - 10
Signal Processing - November 2017 - 11
Signal Processing - November 2017 - 12
Signal Processing - November 2017 - 13
Signal Processing - November 2017 - 14
Signal Processing - November 2017 - 15
Signal Processing - November 2017 - 16
Signal Processing - November 2017 - 17
Signal Processing - November 2017 - 18
Signal Processing - November 2017 - 19
Signal Processing - November 2017 - 20
Signal Processing - November 2017 - 21
Signal Processing - November 2017 - 22
Signal Processing - November 2017 - 23
Signal Processing - November 2017 - 24
Signal Processing - November 2017 - 25
Signal Processing - November 2017 - 26
Signal Processing - November 2017 - 27
Signal Processing - November 2017 - 28
Signal Processing - November 2017 - 29
Signal Processing - November 2017 - 30
Signal Processing - November 2017 - 31
Signal Processing - November 2017 - 32
Signal Processing - November 2017 - 33
Signal Processing - November 2017 - 34
Signal Processing - November 2017 - 35
Signal Processing - November 2017 - 36
Signal Processing - November 2017 - 37
Signal Processing - November 2017 - 38
Signal Processing - November 2017 - 39
Signal Processing - November 2017 - 40
Signal Processing - November 2017 - 41
Signal Processing - November 2017 - 42
Signal Processing - November 2017 - 43
Signal Processing - November 2017 - 44
Signal Processing - November 2017 - 45
Signal Processing - November 2017 - 46
Signal Processing - November 2017 - 47
Signal Processing - November 2017 - 48
Signal Processing - November 2017 - 49
Signal Processing - November 2017 - 50
Signal Processing - November 2017 - 51
Signal Processing - November 2017 - 52
Signal Processing - November 2017 - 53
Signal Processing - November 2017 - 54
Signal Processing - November 2017 - 55
Signal Processing - November 2017 - 56
Signal Processing - November 2017 - 57
Signal Processing - November 2017 - 58
Signal Processing - November 2017 - 59
Signal Processing - November 2017 - 60
Signal Processing - November 2017 - 61
Signal Processing - November 2017 - 62
Signal Processing - November 2017 - 63
Signal Processing - November 2017 - 64
Signal Processing - November 2017 - 65
Signal Processing - November 2017 - 66
Signal Processing - November 2017 - 67
Signal Processing - November 2017 - 68
Signal Processing - November 2017 - 69
Signal Processing - November 2017 - 70
Signal Processing - November 2017 - 71
Signal Processing - November 2017 - 72
Signal Processing - November 2017 - 73
Signal Processing - November 2017 - 74
Signal Processing - November 2017 - 75
Signal Processing - November 2017 - 76
Signal Processing - November 2017 - 77
Signal Processing - November 2017 - 78
Signal Processing - November 2017 - 79
Signal Processing - November 2017 - 80
Signal Processing - November 2017 - 81
Signal Processing - November 2017 - 82
Signal Processing - November 2017 - 83
Signal Processing - November 2017 - 84
Signal Processing - November 2017 - 85
Signal Processing - November 2017 - 86
Signal Processing - November 2017 - 87
Signal Processing - November 2017 - 88
Signal Processing - November 2017 - 89
Signal Processing - November 2017 - 90
Signal Processing - November 2017 - 91
Signal Processing - November 2017 - 92
Signal Processing - November 2017 - 93
Signal Processing - November 2017 - 94
Signal Processing - November 2017 - 95
Signal Processing - November 2017 - 96
Signal Processing - November 2017 - 97
Signal Processing - November 2017 - 98
Signal Processing - November 2017 - 99
Signal Processing - November 2017 - 100
Signal Processing - November 2017 - 101
Signal Processing - November 2017 - 102
Signal Processing - November 2017 - 103
Signal Processing - November 2017 - 104
Signal Processing - November 2017 - 105
Signal Processing - November 2017 - 106
Signal Processing - November 2017 - 107
Signal Processing - November 2017 - 108
Signal Processing - November 2017 - 109
Signal Processing - November 2017 - 110
Signal Processing - November 2017 - 111
Signal Processing - November 2017 - 112
Signal Processing - November 2017 - 113
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Signal Processing - November 2017 - 115
Signal Processing - November 2017 - 116
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Signal Processing - November 2017 - 120
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Signal Processing - November 2017 - 125
Signal Processing - November 2017 - 126
Signal Processing - November 2017 - 127
Signal Processing - November 2017 - 128
Signal Processing - November 2017 - 129
Signal Processing - November 2017 - 130
Signal Processing - November 2017 - 131
Signal Processing - November 2017 - 132
Signal Processing - November 2017 - 133
Signal Processing - November 2017 - 134
Signal Processing - November 2017 - 135
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Signal Processing - November 2017 - 137
Signal Processing - November 2017 - 138
Signal Processing - November 2017 - 139
Signal Processing - November 2017 - 140
Signal Processing - November 2017 - 141
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Signal Processing - November 2017 - 147
Signal Processing - November 2017 - 148
Signal Processing - November 2017 - 149
Signal Processing - November 2017 - 150
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Signal Processing - November 2017 - 153
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Signal Processing - November 2017 - 169
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Signal Processing - November 2017 - 175
Signal Processing - November 2017 - 176
Signal Processing - November 2017 - Cover3
Signal Processing - November 2017 - Cover4
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