IEEE Wireless Communications - April 2017 - page 100

IEEE Wireless Communications • April 2017
98
1536-1284/17/$25.00 © 2017 IEEE
Chunxiao Jiang is with the
Tsinghua Space Center.
Y. Ren is with Tsinghua
University.
Haijun Zhang is with the
University of Science and
Technology Beijing, China
Zhu Han is with the
University of Houston.
Kwang-Cheng Chen is with
the University of South
Florida
Lajos Hanzo is with the
University of Southampton.
A
bstract
Next-generation wireless networks are expect-
ed to support extremely high data rates and
radically new applications, which require a new
wireless radio technology paradigm. The chal-
lenge is that of assisting the radio in intelligent
adaptive learning and decision making, so that
the diverse requirements of next-generation wire-
less networks can be satisfied. Machine learning
is one of the most promising artificial intelligence
tools, conceived to support smart radio terminals.
Future smart 5G mobile terminals are expected
to autonomously access the most meritorious
spectral bands with the aid of sophisticated spec-
tral efficiency learning and inference, in order to
control the transmission power, while relying on
energy efficiency learning/inference and simul-
taneously adjusting the transmission protocols
with the aid of quality of service learning/infer-
ence. Hence we briefly review the rudimentary
concepts of machine learning and propose their
employment in the compelling applications of
5G networks, including cognitive radios, massive
MIMOs, femto/small cells, heterogeneous net-
works, smart grid, energy harvesting, device-to-
device communications, and so on. Our goal is
to assist the readers in refining the motivation,
problem formulation, and methodology of pow-
erful machine learning algorithms in the context
of future networks in order to tap into hitherto
unexplored applications and services.
I
ntroduction
Radical and sometime even un-orthodox next-gen-
eration networking concepts have received sub-
stantial attention both in the academic as well as
industrial communities. One of their driving forces
is that of providing unprecedented data rates for
supporting radical new applications. Specifically,
next-generation networks are expected to learn
the diverse and colorful characteristics of both
the users’ ambience as well as human behavior,
in order to autonomously determine the opti-
mal system configurations. These smart mobile
terminals have to rely on sophisticated learning
and decision-making. Machine learning, as one
of the most powerful artificial intelligence tools,
constitutes a promising solution [1]. As shown in
Fig. 1, we may envision an intelligent radio that
is capable of autonomously accessing the avail-
able spectrum with the aid of learning, altruistical-
ly controlling transmission power for the sake of
conserving energy as well as adjusting the trans-
mission protocols.
Machine learning has found wide-ranging
applications in image/audio processing, finance
and economics, social behavior analysis, project
management, and so on [2]. Explicitly, a machine
learns the execution of a particular task
T
, with
the goal of maintaining a specific performance
metric
P
, based on a particular experience
E
,
where the system aims to reliably improve its
performance
P
while executing task
T
, again by
exploiting its experience
E
. Depending on how
we specify
T
,
P
, and
E
, the learning might also be
referred to as data mining, autonomous discov-
ery, database updating, programming by example,
and so on [3]. Machine learning algorithms can
be simply categorized as supervised and unsuper-
vised learning, where the adjectives “supervised/
unsupervised” indicate whether there are labeled
samples in the database. Later, reinforcement
learning emerged as a new category that was
inspired by behavioral psychology. It is concerned
with an agent’s certain form of reward/utility, who
is connected to its environment via perception
and action. The family of machine learning algo-
rithms can also be categorized based on their sim-
ilarity in terms of their functionality and structure,
yielding regression algorithms, instance-based
algorithms, regularization algorithms, decision tree
algorithms, Bayesian algorithms, clustering algo-
rithms, association rule based learning algorithms,
artificial neural networks, deep learning algo-
rithms, dimension reduction algorithms, ensem-
ble algorithms, and so on. In this article, we will
introduce the basic concept of machine learning
algorithms and the corresponding applications
according to the category of supervised, unsuper-
vised, and reinforcement learning.
Machine learning can be widely used in model-
ing various technical problems of next-generation
systems, such as large-scale MIMOs, device-to-
device (D2D) networks, heterogeneous networks
constituted by femtocells and small cells, and so
on. Figure 2 portrays the family-tree of machine
learning techniques and their potential applica-
tions in 5G. Against this background, we embark
on investigating the family of learning techniques.
Specifically, in the following sections we consider
supervised learning, unsupervised learning, and
C
hunxiao
J
iang
, H
aijun
Z
hang
, Y
ong
R
en
, Z
hu
H
an
,
K
wang
-C
heng
C
hen
,
and
L
ajos
H
anzo
M
achine
L
earning
P
aradigms
for
N
ext
-G
eneration
W
ireless
N
etworks
Digital Object Identifier:
10.1109/MWC.2016.1500356WC
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ccepted
from
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pen
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