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ENHANCING SPECTRAL EFFICIENCY FOR
LTE-A D VA N C E D A N D B E Y O N D C E L L U L A R N E T W O R K S
ENHANCING SPECTRAL-ENERGY EFFICIENCY FOR
LTE-ADVANCED HETEROGENEOUS NETWORKS:
A USERS SOCIAL PATTERN PERSPECTIVE
XING ZHANG, YAN ZHANG, RONG YU, WENBO WANG, AND MOHSEN GUIZANI
ABSTRACT
The development of LTE-Advanced and
beyond cellular networks is expected to offer
considerably higher data rates than the existing
3G networks. Among the many potential technologies in LTE-Advanced systems, users’ characteristics and social behavior have been studied
to improve the networks’ performance. In this
article we present the concept of user social pattern (USP), which characterizes the general user
behavior, pattern, and rules of a group of users
in a social manner, and utilize USP as an optimization basis for network performance
enhancement. From large-scale traffic traces collected from current mobile cellular networks, the
USP model is evaluated and verified. Furthermore, to evaluate the potential of spectral efficiency and energy efficiency enhancement based
on USP in LTE-A HetNets, we establish a complete system and link-level HetNet simulation
platform according to 3GPP LTE-A standards.
Then, based on the platform, simulations are
performed to evaluate the impact of USP on
spectral and energy efficiency in an LTE-A network, and a USP-based spectral efficiency and
energy efficiency enhancement scheme is proposed for a HetNet of the LTE-A system. Simulation results validate that USP can be used as
an effective concept for network performance
optimization in an LTE-A system.
Xing Zhang and Wenbo
Wang are with Beijing
University of Posts and
Telecommunications.
Yan Zhang is with Simula
Research Laboratory.
Rong Yu is with Guangdong University of Technology.
Mohsen Guizani is with
Qatar University.
10
INTRODUCTION
During recent years and with the development
of smart phones such as iPhone/Android, wireless data and multimedia traffic have been
increasing exponentially, particularly at hotspot/
indoor areas. To meet the rapid growth and
demands for high-data-rate wireless broadband
services, significant effort has been made toward
the development of Long Term EvolutionAdvanced (LTE-A) networks, which are expected to offer considerably higher data rates than
the existing third generation (3G) networks.
Meanwhile, as services migrate from voice cen-
1536-1284/14/25.00 © 2014 IEEE
tric to data and multimedia centric, which
requires increased link budget and coverage
extension to provide uniform user experience,
traditional networks optimized for homogeneous
traffic face unprecedented challenges to meet
the various demands effectively. Most recently,
the Third Generation Partnership Project
(3GPP) LTE-A has started a new study to investigate heterogeneous network (HetNet) [1, 2]
deployments as an efficient way to improve the
system spectral efficiency as well as effectively
enhance network coverage. Under this architecture, a number of wireless technologies such as
small cell enhancement [3], device-to-device
(D2D) [4], and multicell cooperation have been
proposed and/or developed in order to enhance
spectral and energy efficiency (SE and EE).
Especially in the architecture of an LTE-A HetNet, the deployment and configuration of small
cells is a key technology to provide high capacity,
good coverage, and high SE.
In the last several years, social networks [5]
have attracted billions of active users, and the
number of users are increasing exponentially.
Social networking applications such as Facebook, Twitter, and Micro-blog are providing
valuable social information on contacts and their
relationships. For example, some users exhibit a
kind of social pattern/behavior and tend to operate with similar characteristics. More and more
research interest has focused on users and traffic
characteristics to optimize cellular networks. Different from the study of connections among
users for traditional social networking [6], in this
article we consider the spatial and temporal
characteristics of users in cellular networks by
taking into account their social characteristics.
Due to the social nature of human habits, the
probability that users close in vicinity (e.g., in the
coverage of one or several base stations) have
similar habits, pattern/behavior, and mobility
rules will be high. On the other hand, because of
the convergent characteristics of LTE-A HetNets with small cell enhancement, it is very suitable for the optimization of such a system to
exploit rules of user behavior. For instance, in
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Core network
aGW
S1
Macro UE
SeNB
Small UE
SeNB
Macro UE
SeNB
Small UE
X2
MeNB
SeNB
MeNB
Small UE
Macro UE
Macro UE
Macro UE
Macro UE
X2
Macro UE
X2
Macro UE
SeNB
Small UE
User group A
To meet rapid growing
demands for high
data-rate wireless
broadband services,
a significant effort has
been made toward the
development of
LTE-advanced networks,
which are expected to
offer considerably higher
data rates than the
existing 3G networks.
MeNB:
Macrocell eNB
SeNB:
Small-cell eNB
MeNB
SeNB
User group B
Macro UE:
Macrocell UE
Small UE:
Small-cell UE
Figure 1. Architecture of heterogeneous networks for an LTE-Advanced system.
[8, 9], two energy-efficient transmission control
schemes in cellular networks for real-time and
best-effort services are proposed, exploiting the
user pattern/behavior. In this way, understanding
and modeling such user patterns is crucial for
the design of LTE-A HetNet systems and services, especially for the deployment and configuration of small cells. User patterns can be used
as the basis for the performance optimization of
the LTE-A system.
Motivated from social group users and the
Gini coefficient [10], we present the concept of
the user social pattern (USP), which characterizes the general user behavior, pattern, and rules
of a group of users as a social entity in cellular
networks, such as user requirements (services/
applications), users’ social characteristics, user
traffic fluctuations (in both temporal and spatial
domain), and users’ mobility. Furthermore, different from the current technologies to enhance
SE, we study a new solution for SE and EE
enhancements from a USP perspective, specifically for the LTE-A HetNet architecture.
The scope of this article is hence to study SE
and EE enhancement in an LTE-A HetNet system from the USP point of view, and to explain
how user social patterns can be exploited to
optimize and analyze LTE-A networks. First, we
introduce the architecture of an LTE-A system
and HetNets with small cell enhancement. Then
the concept of the USP is presented for LTE-A
HetNet enhancement, which is further demonstrated from real traffic traces collected from
cellular networks. We analyze and evaluate the
SE and EE for LTE-A HetNets, exploiting USPs
extensively through simulations.
IEEE Wireless Communications • April 2014
HETEROGENEOUS NETWORKS FOR THE
LTE-ADVANCED SYSTEM
To meet rapidly growing demands for high-datarate wireless broadband services, significant
effort has been made toward the development of
LTE-A networks, which are expected to offer
considerably higher data rates than the existing
3G networks. In order to meet the requirements
of LTE-A (e.g., peak data rates of up to 1 Gb/s),
more spectrum bands are needed. Besides the
existing carriers for 3G networks, spectrum
bands located at 450–470 MHz, 698–790 MHz,
2.3–2.4 GHz, and 3.4–3.6 GHz can be used for
the deployment of LTE and LTE-A networks.
Moreover, LTE-A has been defined to support
scalable carrier bandwidth exceeding 20 MHz,
potentially up to 100 MHz, in a variety of carriers for deployments. The current radio access
network (RAN) for LTE-A consists of a single
node, that is, the eNodeB (eNB) that provides
the user plane and control plane protocol terminations toward the user equipment (UE). It is a
fully distributed RAN architecture, where eNBs
may be interconnected with each other by means
of the X2 interface. Meanwhile, eNBs are connected through the S1 interface to the core network. In each eNB, there exist the physical
(PHY), medium access control (MAC), radio
link control (RLC), and Packet Data Control
Protocol (PDCP) layers that implement the
functionality of user plane header compression
and encryption. Network coverage and highdata-rate requirements in hotspot and indoor
environments have brought new challenges to
LTE systems.
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Many network
configurations can be
determined based on
the users’ characteristics, e.g., the optimal
density of small cells,
the sleeping mode
control for small cells,
homogeneous/
heterogeneous CoMP,
and the coordination of
macro and small cells.
In recent studies and in the standard specifications (e.g., 3GPP R11 and R12), in order to further improve the system capacity and EE,
especially for hotspot and indoor environments,
HetNets and their enhancements have been proposed and studied. In Fig. 1 the architecture of a
HetNet for an LTE-Advanced system is illustrated.
In order to meet the traffic and QoS requirements, the SE and EE of HetNets should be
improved considerably compared to current 3G
and LTE systems. Among the different technologies that have been proposed and studied in
3GPP HetNet, small cell enhancement is one of
the major techniques. The benefits of using
small cell enhancement include:
• Flexible small cell deployment according to
users and traffic distributions
• Optimized small cell mobility by reducing
RAN to core network signaling
• Increased data rates by using macro and small
cells together
• Energy saving by using dynamic small cell
sleeping
In Fig. 1, under the coverage of one macrocell eNB (MeNB) there are several hotspot
areas (e.g., offices, shops, and classrooms). In
this situation, several small cells (SeNBs) are
deployed dynamically according to certain criteria (e.g., SE, EE, and quality of experience
[QoE]). In the coverage of one MeNB, due to
the social habits of humans, the users (both
macro UE and small UE) and their traffic volume will be heterogenously distributed and
exhibit a convergent pattern in both the temporal and spatial domains. Under this condition,
these small cells can be managed smartly. Many
network configurations can be determined based
on users’ characteristics (e.g., the optimal density
of small cells, sleeping mode control for small
cells, homogeneous/heterogeneous CoMP, and
the coordination of macro and small cells).
Thus, understanding such user behavior is
very crucial and beneficial for the design of
LTE-A HetNet systems. In the next section, the
USP, describing user distributions and characteristics, is presented and studied.
USER SOCIAL PATTERN:
DEFINITION AND MODELING
MOTIVATION AND CONCEPT OF USP
In current wireless cellular networks, the traffic
requested by different users is diverse and
dynamically changing in both the temporal and
spatial domains. Large amounts of traffic are
requested by users distributed in relatively small
hotspot regions, while much less traffic is
requested by users in other regions. We define
the hotspot density as the ratio of the hotspot
area over the whole coverage area. It denotes
the degree of how much hotspot area there is. In
Fig. 1, user group A and user group B are two
kinds of social groups in office and home areas;
these two groups can be regarded as two
hotspots covered by small cells. These two
groups will probably contribute most of the data
amount in the macro-eNB (MeNB), while the
other area in the MeNB will contribute less traf-
12
fic. Thus, the distributions and characteristics of
these groups will be important for the system
design.
On the other hand, in the time dimension, a
large number of users may request traffic during
peak hours (e.g., 10 a.m.–12 a.m., 20 p.m.–22
p.m.). Besides this, in the service (application)
domain, various services (applications) may
gather in various regions for a certain (short)
time period. This user pattern exhibits strong
social characteristics, and we want to find a simple yet effective parameter or metric to describe
this inhomogeneous phenomenon. Here, we
denote this as USP, which characterizes the general user behavior, and the patterns and rules of
a group of users in a social manner. To mathematically describe the USP, we take the Gini
coefficient [10] in statistics and economics as a
reference. The Gini coefficient (also known as
the Gini index or Gini ratio) is a measure of statistical dispersion intended to represent the
income distribution of a nation’s residents. Similar to incomes, users and traffic in HetNets can
also be described and modeled. In the following
sections, two key parameters for the USP model,
curve r(x) and coefficient h, are described.
MODELING OF A USER SOCIAL PATTERN
Figure 2 shows the illustration of user social pattern. Figures 2a and 2b describe the temporal
and spatial characteristics, respectively. To quantitatively model the proposed USP in heterogeneous networks, we take the spatial domain as
an example. The modeling of the temporal
and/or service/application domains is similar. In
a cellular network with users distributed across
X km × Y km area, as shown in Fig. 2b. The calculation for the curve r(x) is written as follows:
First, we subdivide the X × Y area into N
small regions (these regions can be the same size
of small cells) and obtain the number of users or
traffic volume in each region, that is, {ui, i = 1,
2, …, n}.
Second, according to u i , we sort all the
regions in ascending order as u (i) : u (1) < u (2)
<…< u(n).
Finally, the curve is then obtained as the ratio
of cumulative amount of u (i) and the total
amount, which can be expressed as
ρ (x) =
Σ i ≤ nx u(i )
Σ i ≤ n u(i )
, 0 ≤ x ≤1
Under this process, it is seen that if the users
are more equally distributed, the curve is closer
to the 45˚ line. But when the users are more
convergent (centralized), the curve will be closer
to the lower right corner.
Meanwhile, in order to characterize the
degree of USP based on the definition of Gini
index [1], we further present the coefficient h of
USP based on the curve r(x), whose range is
from 0 to 1. In Fig. 2, the coefficient of USP is
calculated as the ratio of areas A and B [10]
(shown in Fig. 2c),
h = 1−
1
A
= 1 − 2 ∫ ρ ( x ) dx.
0
A+ B
Then the value of coefficient h (0  h  1)
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can be used as a metric of the USP degree. A
larger h shows more user convergence in the
spatial or temporal domain, while a smaller h
shows more equally distributed users.
Temporal domain
Traffic volume for a typical day (24 hours)
80
USP FOR DIFFERENT TRAFFIC DISTRIBUTIONS
Sn-1
70
A smaller coefficient h indicates a more equal
distribution, with zero corresponding to complete equality. A higher coefficient indicates that
users are more centralized, with one corresponding to complete convergence. In this subsection,
USP coefficient h for various probability distributions are presented. For a distribution with
mean m and standard deviation s, for constant
distribution, h = 0. For exponential distribution,
h = 0.5. For lognormal distribution,
Number of users
60
S3
50
S2
40
30
Sn
20
S1
10
0
h = erf ⎛⎜ 0.5 ln ⎡⎣1 + (σ / μ )2 ⎤⎦ ⎞⎟ .
⎝
⎠
0
× 2 F1 (1, 2(σ / μ )
+ 1;(σ / μ )
9
12
16
Time (hour)
(a)
18
21
24
A cellular coverage area of X km * Y km
(σ / μ )−2 Γ ((σ / μ )−2 + 1 / 2)
π
Γ ((σ / μ )−2 + 2)
−2
6
Spatial domain
For Gamma distribution,
h = 1−
3
−2
U1
U2
U3
Non-hotspot
+ 2;1 / 2)
where () is the Gamma function, and 2F1(a, b;
c; z) is the Gaussian hypergeometric function.
In the next subsection, a large amount of traffic data is collected from cellular networks, and
the USP model is studied and verified by real
traffic traces.
Hotspot
Un-1
USP EVALUATION FROM LARGE-SCALE DATA SETS
OF CELLULAR NETWORKS
To evaluate the USP model, massive real traffic
traces of three weeks were collected from
CDMA2000 cellular networks of China Telecom
(one of the three operators in China with more
than 186 million mobile subscribers, 103 million
of which are 3G mobile subscribers until December 2013). Two metropolitan cities in China,
Nanjing and Chengdu, are selected for the USP
evaluation; the former is located in the eastern
region, while the latter is in the far west.
Through analysis we find that the users are distributed according to a Gamma distribution in
the spatial domain.
In Fig. 3, the coefficient h of the USP for theoretical analysis and real data analysis are compared. The theoretical curve is calculated based
on the h equation for the Gamma distribution
earlier, while each dot is calculated based on one
snapshot’s traffic data. From the comparison, we
can see that the coefficient of USP h can fit the
real traffic data of a cellular network well.
Furthermore, for a city, the users’ social characteristics of different suburban, urban, and
dense urban regions may be very different. To
describe the USP for different regions, we compare the USP curves and calculate the h coefficient for Chengdu city regions. Figures 4a and
4b show the 2 km × 2 km urban and dense urban
areas, respectively. The blue (+) dots represent
the locations of base stations; Voronoi cells [11]
are used to decide the coverage of cells based on
IEEE Wireless Communications • April 2014
Hotspot
Un
(b)
2
p(x)
A
0.5
USB curve
0.28
B
0.14
0.06
0
0.2
0.3
0.5
0.8
1
x
(c)
Figure 2. Illustration of user social pattern modeling: a) the temporal
domain: traffic volumes in one typical day; s1, s2 … denotes the user traffic
amount in different time intervals; b) the spatial domain: a cellular coverage area, u1, u2 ... denote the user traffic amount in small regions; c) the
curve for USP r(x).
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1
1
Nanjing
Chengdu
0.9
0.9
0.85
0.85
0.8
0.8
h
0.95
h
0.95
0.75
0.75
0.7
0.7
0.65
0.65
Theory
Real data
Theory
Real data
1
1.5
2
2.5
3
3.5
σ/μ
4
4.5
5
5.5
6
1
2
3
4
5
σ/μ
6
7
8
9
Figure 3. User social pattern coefficient h for spatial evaluation of cellular networks in two cities of China: Nanjing and Chengdu.
The x-axis shows the CV (coefficient of variation, s/m) for a given area of 3 × 6 km, three weeks traffic (Sept. 2012) are collected.
locations of the base stations. The boundaries of
cells are depicted using blue lines. In Figs. 4a
and 4b, the red dots show that users are distributed among Voronoi cells. In Fig. 4c, the
three USP curves are illustrated and compared,
showing that the social pattern for the dense
urban area is more intense (coefficient h =
0.83), while users in the suburban area are more
equally scattered (coefficient h = 0.4).
Through our analysis, the advantages of using
the USP can be summarized as follows:
• Generally describing the users’ social convergence characteristics (inhomogeneous distribution) in the temporal or spatial domain
• Reflecting real users’ behavior based on real
network traffic traces
• Easy to use with only one parameter — coefficient of USP h
ENHANCING SPECTRAL/ENERGY EFFICIENCY
BY EXPLOITING USER SOCIAL PATTERNS
In the current HetNet small cell enhancement
(SCE) evaluation standard [3], in the coverage
of one macrocell, there are several hotspots (socalled small cell cluster), the SeNBs are uniformly distributed among the area of a hotspot,
and the users are uniformly distributed among
the coverage of the SeNB. In this way, hotspot
characteristics of users and the traffic can be
modeled. In this section, we focus on the optimization of HetNet exploiting such non-uniform
characteristics of users and traffic. Spectral and
energy efficiency enhancement exploiting USP is
studied. For a given area with non-uniform distributed users and traffic, based on the user
social pattern (h), proper configuration of HetNet to enhance the spectral efficiency is determined, such as SeNB density lm, MeNB density
lM, transmitting power for SeNB, and MeNB Pm
and PM.
14
SPECTRAL EFFICIENCY IMPROVEMENT
EXPLOITING USER SOCIAL PATTERNS
In this subsection, the performance of the spectral efficiency exploiting USP in an LTEAdvanced HetNet system under different
scenarios is evaluated through OPNET-based
system-level and Matlab-based link-level simulation. The simulation parameters, including the
channel model and system configurations, are
based on 3GPP specifications [12] and the FP7
EARTH project [13]. In the simulation platform,
19 cells/3 sectors per cell are simulated with
wraparound. The inter-site distance (ISD) is
chosen as 500 m. In each MeNB, there are a
fixed number of hotspots uniformly distributed
across the coverage of the MeNB. In each
hotspot there are several users uniformly distributed. The path loss model for MeNB-to-UE
link is 131.12 + 42.8log(R) in dB, R in kilometers; while for SeNB-to-UE link, the line of sight
(LOS) and non-LOS (NLOS) link use 103.8 +
20.9log(R) and 145.4 + 37.5log(R), respectively.
Under these simulation configurations, we can
calculate the USP coefficient h based on the earlier proposed method. To compare the performance of various h, since the total number of
users and hotspots are fixed, we then vary the
coefficient h through changing the coverage of
each hotspot.
In Fig. 5, we compare the spectral efficiency
for different deployments of small cells under various coefficients of USP h. When h increases, the
optimal number of SeNBs for maximizing spectral
efficiency tends to increase. This is because the
coverage of each hotspot will be smaller when h
becomes larger. In this way, the hotspots will be
more isolated. In order to cover these hotspots,
more SeNBs should be needed to achieve the
maximal spectral efficiency. On the other hand, as
shown in Fig. 5, for a given USP coefficient h,
when the number of SeNB N increases, the spec-
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ZHANG1_LAYOUT.qxp_Layout 4/29/14 8:14 PM Page 15
tral efficiency first increases. When N reaches a
certain point, the spectral efficiency begins to
decrease. This can be explained as follows. More
SeNBs will cause severe interference between the
links of SeNB-SeNB and SeNB-MeNB. That is,
for a certain USP, there is an optimal number of
small cells for maximizing spectral efficiency. The
proposed USP can be used as an indicator for
choosing the optimal small cells for the best spectral efficiency. In the practical deployment of an
LTE-Advanced HetNet system, first, the user and
traffic distributions are collected in a given coverage area; then the USP is estimated and the curve
and coefficient h is calculated; and finally, based
on the value of h and configurations of HetNet,
the optimal number of small cells N can be determined as the deployment scheme.
ENERGY EFFICIENCY IMPROVEMENT
EXPLOITING USP
For the energy efficiency improvement of
LTE-A heterogeneous networks, in addition to
the transmitting power of eNBs, static power of
MeNB PMc and SeNB Pmc should be considered.
For an energy-efficient HetNet with capacity C,
the density and transmitting power for MeNB
and SeNB should be optimized, under the condition of USP h and minimum data rate requirement for the cell boundary users, Rmin. We then
formulate the EE problem as
λm ,λ M , Pm , PM
Subject to
EE =
C
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
0
IEEE Wireless Communications • April 2014
1
1.2
1.4
1.6
1.8
1.2
1.4
1.6
1.8
0.8
0.9
2
1.6
1.4
1.2
0.8
High spectral efficiency and energy efficiency
are expected for LTE-A HetNets. Among the
many technologies to improve the performance
of LTE-A HetNet systems, enhancement from
insight into users’ characteristics is a new
0.8
1.8
λ M ( PM + PMc )
CONCLUSION
0.6
Dense urban
1
We can use the classic Lagrange multiplier
method to solve this optimization problem; the
network configurations in an LTE-A HetNet system (i.e., lm, lM, Pm, PM) can be determined to
improve the EE.
For the baseline scenario without using USP
in HetNet, the density of SeNB l m is chosen
based on the density of the number of hotspots.
The transmitting power is also fixed; in this case
the EE can also be calculated based on Eq. 2. In
Fig. 6, we show the energy efficiency improvement of our HetNet configuration scheme compared to that without using the USP. Three
different scenarios with low, medium, and high
hotspot densities are studied, the hotspot density
denoting the degree of hotspot area in a cell. It
is shown that the EE can be improved by an
average of 80 percent when the USP coefficient
h = 0.4. While users are more convergent (i.e., h
= 0.6), the improvement can reach more than
200 percent. Meanwhile, it is easily seen that for
a certain h, the EE improvement becomes higher when the hotspot density is large.
0.4
2
λ m ( Pm + Pmc ) +
h; Rmin .
0.2
(a)
0.6
0.4
0.2
0
0
0.2
0.4
0.6
0.8
1
2
(b)
Suburban: h=0.40
Urban: h=0.75
Dense urban: h=0.83
1
0.9
0.8
0.7
0.6
p(x)
max
Urban
2
0.5
0.4
0.3
0.2
0.1
0
0.1
0.2
0.3
0.4
0.5
0
0.6
0.7
1
x
(c)
Figure 4. a) Urban; b) dense urban scenarios; c) user social pattern. The
areas are 2 km × 2 km; the cell boundary is processed using Voronoi cells.
15
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16
effective basis for network performance optimization in LTE-A systems.
h=0.80
h=0.65
h=0.40
ACKNOWLEDGMENT
This research is supported by the National 973
Program of China under grant 2012CB316005,
by the National Science Foundation of China
(NSFC) (61372114, 61370159, U1035001,
U1201253), by the Beijing Higher Education
Young Elite Teacher Project (No.YETP0434),
by the European Commission FP7 Project
EVANS (2010-269323), and by the SmartGrids
ERA-Net project PRO-NET funded through the
Research Council of Norway (project 217006).
This work has been partially supported by the
European Commission COST Actions IC0902,
IC0905, and IC1004.
Spectral efficiency (bps/Hz)
14
12
10
8
Optimal number of SeNBs
for maximizing SE
6
REFERENCES
4
10
5
15
20
Number of SeNBs
25
30
Figure 5. Spectral efficiency for user social pattern under different number
of SeNBs.
250
Energy efficiency improvement [%]
Hopspot density=9%
Hopspot density=15%
Hopspot density=23%
200
150
100
50
0
0.4
0.5
h
0.6
Figure 6. Energy efficiency (EE) improvement exploiting user social pattern.
The y-axis shows EE improvement [%] compared to the scheme without
using USP.
paradigm. In this article, the concept of a user
social pattern (USP) is presented to characterize
the general user behavior, pattern, and rules of a
group of users in a social manner, which is further evaluated based on real traffic traces collected from mobile cellular networks. Our results
show that the proposed USP model can effectively describe the convergence phenomenon of
humans in cellular networks, which is utilized as
an optimization metric for system performance
improvement. Based on the model of USP, USPbased spectral efficiency and energy efficiency
enhancement schemes are proposed and evaluated for LTE-A HetNet systems. Simulation results
validate that the USP concept can be used as an
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[3] 3GPP TR 36.932 V12.1.0, “Scenarios and Requirements
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BIOGRAPHIES
X ING Z HANG [M’10] (zhangx@ieee.org) received his Ph.D.
degree from Beijing University of Posts and Telecommunications (BUPT), China, in 2007. Since July 2007, he has
been with the School of Information and Communications
Engineering, BUPT, where he is currently an associate professor. His research interests are mainly wireless communications and networks, green communications and
energy-efficient design, cognitive radio and cooperative
communications, traffic modeling, and network optimization. He is the author/co-author of two technical books
and has published more than 40 papers in top journals
and international conferences, and filed 25 patents (11
granted). He has served as a TPC member for a number of
major international conferences, including IEEE ICC, IEEE
GLOBECOM, and the Wireless Communications and Networking Conference (WCNC).
YAN ZHANG [SM’10] (yanzhang@ieee.org) received a Ph.D.
degree from Nanyang Technological University, Singapore.
He works at Simula Research Laboratory, Norway, and is an
adjunct associate professor at the University of Oslo, Norway. He is an Associate Editor or Guest Editor of a number
of international journals. He serves as an Organizing Committee Chair for many international conferences. His
IEEE Wireless Communications • April 2014
ZHANG1_LAYOUT.qxp_Layout 4/29/14 8:14 PM Page 17
research interests include resource, mobility, spectrum,
energy, and data management in wireless communications
and networking.
RONG YU [S’05, M’08] (yurong@ieee.org) received his Ph.D.
from Tsinghua University, China, in 2007. After that, he
worked in the School of Electronic and Information Engineering of South China University of Technology (SCUT). In
2010, he joined the Institute of Intelligent Information Processing at Guangdong University of Technology (GDUT),
where he is now a full professor. His research interest
mainly focuses on wireless communications and mobile
computing, including cognitive radio, machine-to-machine
communications, wireless sensor networks, home networks, and vehicular networks. He is a co-inventor of 15
patents, and author or co-author of over 70 international
journal and conference papers. He is currently serving as
the Deputy Secretary General of the Internet of Things (IoT)
Industry Alliance, Guangdong, China, and Deputy Head of
the IoT Engineering Center, Guangdong, China. He is a
member of the Home Networking Standard Committee in
China, where he leads the standardization work of three
standards.
WENBO WANG (wbwang@bupt.edu.cn) received B.S., M.S.,
and Ph.D. degrees from BUPT in 1986, 1989, and 1992,
respectively. He is currently a professor and executive vice
dean of the Graduate School, BUPT. Currently, he is the
assistant drector of the Key Laboratory of Universal Wireless Communication, Ministry of Education. He has published more than 200 journal and international conference
papers, and six books. His current research interests include
IEEE Wireless Communications • April 2014
radio transmission technology, wireless network theory,
and software radio technology.
M O H S E N G U I Z A N I [S’85, M’89, SM’99, F’09]
(mguizani@ieee.org) is currently a professor and associate
vice president for Graduate Studies at Qatar University,
Doha. He was chair of the Computer Science Department
at Western Michigan University from 2002 to 2006, and of
the Computer Science Department at the University of
West Florida from 1999 to 2002. He also served in academic positions at the University of Missouri-Kansas City,
University of Colorado-Boulder, Syracuse University, and
Kuwait University. He received his B.S. (with distinction)
and M.S. degrees in electrical engineering, and M.S. and
Ph.D. degrees in computer engineering in 1984, 1986,
1987, and 1990, respectively, from Syracuse University,
New York. His research interests include wireless communications and mobile computing, security, cloud computing,
and smart grid. He currently serves on the editorial boards
of six technical Journals, and is the Founder and Editor-inChief of the Wireless Communications and Mobile Computing” Journal (Wiley) (http://www.interscience.wiley.com/
jpages/1530-8669/). He is the author of eight books and
more than 300 publications in refereed journals and conferences. He guest edited a number of special issues in
IEEE journals and magazines. He has also served as a member, chair, and general chair of a number of conferences.
He served as Chair of the IEEE Communications Society
Wireless Technical Committee (WTC) and of the TAOS Technical Committee. He was an IEEE Computer Society Distinguished Lecturer from 2003 to 2005. He is a Senior
member of ACM.
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