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Proceedings ArticleDOI

Carrier components assignment method for LTE and LTE-A systems based on user profile and application

01 Dec 2014-pp 1020-1025
TL;DR: A novel Carrier Component assignment method which considers user profiles and traffic types to increase quality of services and experiences getting by mobile users is proposed and can provide improved throughput rate in LTE and LTE-A systems.
Abstract: Increasing number of mobile users accesses large multimedia files (such as high definition audio, video, images, etc.) over the Internet. Therefore, the bandwidth demand for mobile Internet access is getting exponentially larger. To answer users' demand, Carrier Aggregation is proposed in LTE-A. In Carrier Aggregation, multi bands are used and the bands have supported different coverage. Therefore, mobile users can simultaneously connect only one or multi bands. Because of mobility of users, traffic types and assigned channel errors, the best available Carrier Components of each band should be assigned to each user in order to increase quality of services. Several works have been proposed in the literature to address Carrier Components assignment to mobile users by using Channel Quality Indicator, quality of service and traffic types in LTE systems. However, continuously increasing data requests of users forces the operators to manage traffic more intelligently. Therefore, we have proposed a novel Carrier Component assignment method which considers user profiles and traffic types to increase quality of services and experiences getting by mobile users. Results show that the proposed method uses system resources efficiently and can provide improved throughput rate in LTE and LTE-A systems. Our method will help service providers build efficient Carrier Component assignment services through considering user profile and traffic types.

Summary (3 min read)

Introduction

  • Therefore, the bandwidth demand for mobile Internet access is getting exponentially larger [3].
  • Results show that the proposed CCs assignment method uses system resources efficiently and can provide improved throughput rate in LTE and LTE-A.
  • In Section V, simulation results are analyzed.

II. SYSTEM MODEL AND USER PROFILE

  • In Fig. 1, User Equipments (UEs) are mobile.
  • UEs can connect one band or multi bands simultaneously based on coverages of bands and UEs’ positions.
  • Above scenarios show the importance of management of CCs in LTE and LTE-A in order to increase performance.

A. System Model

  • Fig. 2 shows system model for a CCs assignment method.
  • One to two of CCs are primary component carrier (PCC) for DL and UL, and can only be updated during handover [4], but the rest of CCs can be dynamically assigned to each UE in specified time interval [19].
  • Determining the number of required CCs and band of each CCs for each UE does not only decrease power consumption and interference but also increase efficiency of CCs resources usage.
  • Estimating RT and NRT data usage for a UE helps an eNB arrange the number of CCs and their bandwidth sizes, and estimating mobility of a UE reduces handover overheads and risk of connection lost.
  • It is important to note that the user profile can be used with any existing CCs assignment methods.

B. User Profile Detection Based on Services

  • Historical data usage information of each UE plays crucial role to identify user profiles.
  • As shown in Table II, each UE holds Times, Connection Time (Con. T) and Idle Time (Idle T.), RT and NRT services data sizes for each eNB.
  • In order to identify user profile from Table II, some statical analysis such as percentage measurement, can applied.
  • CCS ASSIGNMENT METHOD FOR LTE\LTE-A SYSTEM Fig. 3 illustrates the proposed CCs assignment method in LTE systems.
  • Simply, the proposed method firstly finds the number of required CCs and bands of CCs, and assigns them to each UE.

A. Assumptions for eNBs

  • While implementing simulation, it is assumed that there is only one eNB which has three bands to provide service to UEs.
  • CCs for NRT and RT services and their sizes and quantities are given in Table III.
  • The sizes and quantities are arranged based on the 800MHz, 1.8GHz and 2.6GHz.
  • To reach required data rate for LTE systems, 10MHz bandwidth is chosen for NRT services and 20MHz bandwidth is chosen for RT services from Band-b and Band-c, and only 10MHz bandwidth is chosen for RT and NRT services from Band-a because PCC is generally chosen from a band which has higher range like Band-a.
  • In addition, bandwidth size of NRT type CCs is 10MHz because RT traffic data usage is more common than NRT data usage for mobile devices.

B. Assumptions for UEs

  • There are three types of UEs, LTE, LTE-A low and LTE-A full capacities in the system.
  • UEs are uniformly distributed in area and UEs can use one or multi bands.
  • Packet arrivals follow Poisson distribution and arrival rates of traffic are getting higher when the number of users is increased.
  • Selected Transmission Time Interval (TTI) for a packet is 1ms.

C. Observation Methodology

  • The simulation results in Section V are average of 1000 simulation runs for different UEs size.
  • Only data usage estimation based on user profile is used with simple mobility estimation in order to show effects of the proposed method on R method.

D. Packet Scheduling

  • Without packet scheduling, the result cannot be obtained.
  • Therefore, the authors have used a simple packet scheduling method in order to compare RSA and UPRs (UPRs represents UPR, UPR10 and UPR25 together).
  • For test case, predetermined static number of CCs is four for RSA because maximum number of CCs for each UE is five in LTE systems and one of them must be used for PCC (see Section II).
  • RSA and UPRs transfer each packet by using one of assigned CCs.
  • If there are multiple available CCs from different bands, firstly CCs which belongs to lower range band (Band-c) are preferred to transfer the packet in order to decrease traffic loads to higher range band (Band-a).

V. RESULTS

  • Utilization of bands is measured by dividing total packets of active users in each CC to total capacity of CCs in each band then averaging the result with total time steps (simulation time/10ms).
  • Throughput rates are measured by dividing transferred packets to all generated packets for NRT and RT.
  • Therefore, while increasing number of UEs, throughput of traffic decreases for each UE.
  • By these comparisons, resource usage and managed QoS can be compared.
  • The method which has lower resource usage and higher throughput with equal fair service between device types is better.

A. Utilization

  • Figs. 4, 5 and 6 show the utilization for Band-a, Band-b and Band-c, respectively, obtained for RSA and UPRs.
  • If utilization of Band-a, Band-b and Band-c are compared, it is observed that while the number of UEs is getting higher, utilization of all bands is gradually increasing for all cases.
  • Utilization of Band-a is increasing faster than utilization of Band-b, utilization of Band-b is increasing faster than utilization of Band-c for all cases.
  • There are three reasons for it: (i) bands which have higher range are used more than bands which have lower range, (ii) distribution of UEs around the eNB increases probability of lower amount of UEs located in bands which have lower range and vice verse, and (iii) CCs assignment based on R method without considering CCs loads.
  • Bands utilization results of UPRs are close to each other for all bands while even error rate is increased to %10 and %25.

B. Throughput Rate

  • While the number of UEs is increased, throughput rate per UE is decreasing for both traffic type.
  • While user profile has error rate up to %25, throughput rates of UPRs are almost equal and higher than RSA for NRT and RT traffics.
  • When the number of UEs is reached to 250, NRT throughput are almost equal for all cases.

C. Fairness

  • Fig. 9 show the service fairness between device types.
  • L represents LTE and LTE-A-Low capacity devices while F represents LTE-A full capacity devices.
  • By using RSA, LTEA full capacity devices get more service than LTE and LTE-A low capacity devices.
  • UPRs are capable to fairly distribute service to UEs.

VI. CONCLUSION

  • The authors have proposed a carrier component assignment method for LTE and LTE-A systems by considering user profiles.
  • Throughput of non-real time and real time traffic, and bands utilization have been compared through extensive simulations.
  • Results show that the proposed method uses system resources efficiently and provides improved user throughput rate and utilization in LTE and LTE-A systems.
  • The proposed method and related analysis will help service providers build efficient LTE-A systems architectures which are adaptable to LTE and LTE-A type devices by considering user profile, traffics and bands performances, such as throughput and utilization.

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Carrier Components Assignment Method for LTE
and LTE-A Systems Based on User Profile and
Application
Husnu S. Narman and Mohammed Atiquzzaman
School of Computer Science, University of Oklahoma, Norman, OK 73019
Email: {husnu, atiq}@ou.edu
Abstract—Increasing number of mobile users accesses large
multimedia files (such as high definition audio, video, images, etc.)
over the Internet. Therefore, the bandwidth demand for mobile
Internet access is getting exponentially larger. To answer users’
demand, Carrier Aggregation is proposed in LTE-A. In Carrier
Aggregation, multi bands are used and the bands have supported
different coverage. Therefore, mobile users can simultaneously
connect only one or multi bands. Because of mobility of users,
traffic types and assigned channel errors, the best available
Carrier Components of each band should be assigned to each
user in order to increase quality of services. Several works have
been proposed in the literature to address Carrier Components
assignment to mobile users by using Channel Quality Indicator,
quality of service and traffic types in LTE systems. However, con-
tinuously increasing data requests of users forces the operators to
manage traffic more intelligently. Therefore, we have proposed a
novel Carrier Component assignment method which considers
user profiles and traffic types to increase quality of services
and experiences getting by mobile users. Results show that
the proposed method uses system resources efficiently and can
provide improved throughput rate in LTE and LTE-A systems.
Our method will help service providers build efficient Carrier
Component assignment services through considering user profile
and traffic types.
Index Terms—LTE, LTE-A, user profile, carrier component
assignment, nonreal time traffic, real time traffic, simulation
I. INTRODUCTION
Usage of Mobile Devices (MD) (such as tablet, smart-
phones, etc.) is increasing significantly and the number of
MD for 2013 passed one billion and the expected number
of MD for 2017 is almost two billions [1]. The report [1]
shows that smartphones and tablets will dominate the future
personal computer device market. The most notable reason
of increasing MD is that MD users can reach wide range
of applications under different platforms (e.g., GooglePlay,
AppStore) [1] by cutting cross time and place restriction [1],
[2]. More than hundred billions mobile applications have
been downloaded and more than 250 billions applications are
expected to be downloaded for 2017 [1].
Increasing number of mobile users [1] accesses large mul-
timedia files (such as high definition audio, video, images,
etc.) over the Internet. Therefore, the bandwidth demand for
mobile Internet access is getting exponentially larger [3]. To
answer users’ demand, Carrier Aggregation (CA) is proposed
to extend bandwidth and support 1.5 Gbps for uplink and 3
Gbps for downlink peak data rates in LTE-A [4]. In Carrier
Aggregation multi bands are used and the bands have sup-
ported different ranges (i.e., here range means coverage area
of each band).
Fig. 1 shows the multi-band architecture in mobile networks.
In this architecture, the bands have supported different ranges.
MD users can simultaneously connect one or multi bands as
showed in Fig. 1. Base stations should arrange the number of
simultaneous connections for each band because one band can
be overflowed while the other band can be idle. Because of
Fig. 1. eNodeB (eNB) with multi bands and several UEs.
mobility of users, traffic types and assigned channel errors, the
best available Carrier Components (CCs) of each band should
be assigned to each user to increase quality of services [5].
Because of recent improvements in LTE systems, there are
several proposed CCs assignment methods [6]–[18] in the lit-
erature. In [12], a method is proposed to measure the Channel
Quality Indicator (CQI) in LTE-A. In [6]–[9], full or partial
feedback is used for CQI to find the best available carrier for
each user. In [11], distribution of carriers to users are balanced.
In [17], uplink (UL) CA has been proposed by considering
a ratio function, traffic type and CQI to increase throughput
while sending data from user to eNodeB (eNB). While uplink
CCs assignment has bandwidth and power limitation, downlink
(DL) CCs assignment has only bandwidth limitation. In [13],
[16], service-based methods for CCs assignment are proposed
by giving priority for some services while assigning CCs
to users. In [19], CCs are dynamically assigned for each

user in specified time interval. In addition to the above CCs
assignment methods, there exist traditional carrier assignment
methods, Least Load (LL) (LL is also called as Round Robin
(RR)) and Random (R) [20]. LL and R are well balance traffic
loads across different carriers while they ignore Quality of
Service (QoS) requirements of each user.
Continuously increasing desired data requests of users
forces the operators to manage traffic more intelligently be-
cause economic and physical limitations do not allow operators
to extend network capacity [21]. Although Load balancing,
QoS and CQI methods, as summarized above, have been
used to manage traffic and CCs assignment, more advance
techniques [21] in addition to these methods will be needed to
satisfy users’ demands in LTE-A. Therefore, the aim of this
work is to propose user profile CCs assignment method in
addition to traffic types in order to manage LTE systems more
intelligently. None of the above works consider user profiles
while assigning CCs to each user. However, not only mobility
of each user profile is different but also each user profile needs
different QoS from different types of traffic [21]. As illustrated
in Table I, bandwidth requirements of each application (Real
Time (RT) and Non-real Time (NRT) services) and mobility of
each user profiles are different (See Table I for Teenager and
Businessman). Therefore, user profiles, in addition to traffic
types, can be considered to increase QoS and Quality of
Experience (QoE).
The objective of this paper is to increase QoS and QoE
getting by mobile users by proposing a CCs assignment
algorithm which considers user profiles and traffic types. The
key contribution of this work are as follows: (i) defining user
profiles with respect to traffic types and mobility, (ii) proposing
a novel CCs assignment algorithm based on user profiles and
traffic types, and (iii) evaluating performance of the proposed
method with extensive simulation.
Results show that the proposed CCs assignment method
uses system resources efficiently and can provide improved
throughput rate in LTE and LTE-A. Therefore, the proposed
method and related analysis will help service providers build
efficient LTE-A systems architectures which are adaptable to
LTE and LTE-A type devices by considering user profile and
different types of traffic performances, such as, throughput.
The rest of the paper is organized as follows. In Section II,
we explain the system model of LTE-A and user profile with
its properties. The proposed method is presented in Section III
and simulation environments with parameters are explained
in Section IV. In Section V, simulation results are analyzed.
Finally, Section VI has the concluding remarks.
II. SYSTEM MODEL AND USER PROFILE
In Fig. 1, User Equipments (UEs) are mobile. UEs can
connect one band or multi bands simultaneously based on
coverages of bands and UEs’ positions. UEs can change
connected bands to another band in the same eNB if they move
from the coverage of one band to the coverage area of another
band. For example, when a UE, which is using Band-b, enters
Band-c range, some of several CCs assignment scenarios for
a UE can be as follows (see Fig. 1): (i) the UE may need
to use larger bandwidth for services, therefore changing its
band to Band-c will increase performance, (ii) mobility of
the UE is high, therefore changing its band to Band-c may
decrease performance because of low range, (iii) the UE does
not need to use larger bandwidth from Band-c, thus no need to
update its band, and (iv) the mobility of the UE is high and the
UE needs larger bandwidth, therefore it can use both bands.
In addition to bands assignment, determining the number of
TABLE I
MOBILE USERS PROFILE
User Profile
Teen. H. wife B. man Grad. Stu. G. parent
Traffic Types
RT
Video V. High Middle Low Medium Low
Onl. Game V. High Low Low Medium Low
Movie V. High V. High Low Medium Low
Talk Low Medium High Medium V. High
NRT
Web High Low V. High Medium Low
Mail High Low V. High Medium Low
SMS V. High Medium Low Medium Low
Con.
Mobility Low Medium V. High Low Low
Location Low Medium High Medium Low
required CCs for each UE is significant because of power and
QoS efficiency. For example, when a UE can enter an eNB
range, some of scenarios to determine the number of CCs for
the UE can be as follows: (i) data usage of the UE is small,
therefore only one CC will be enough, (ii) the data usage of
the UE is high, therefore, assigning multi CCs will increase
performance, and (iii) device type of the UE is not allowed to
assign more than one CC, therefore, one CC will be assigned.
Above scenarios show the importance of management of CCs
in LTE and LTE-A in order to increase performance.
A. System Model
Fig. 2 shows system model for a CCs assignment method.
There are n number of UEs and each UE can only connect up
to m number of CCs. One to two of CCs are primary compo-
nent carrier (PCC) for DL and UL, and can only be updated
during handover [4], but the rest of CCs can be dynamically
assigned to each UE in specified time interval [19]. Today,
LTE-A system can only support five CCs for each UE in
order to provide LTE-A level service [4]. However, assigning
User 1
Traffic Type Classifier
Packets Scheduler
User
Profile
process
CC
1
CC
2
CC
3
CC
m
Arrange number of
CCs
and
assign
CCs
User 2
User 𝑛
Fig. 2. System Model with n users and m available CCs.
all available CCs to a UE can increase power consumption
and interference. Therefore, it is important to have a CCs
assignment method, which firstly determines the number of
required CCs and band of each CCs for each UE then assign
2

them. Determining the number of required CCs and band
of each CCs for each UE does not only decrease power
consumption and interference but also increase efficiency of
CCs resources usage. The only way to find required CCs is
to estimate data usage and mobility of UEs (user profiles).
Estimating RT and NRT data usage for a UE helps an eNB
arrange the number of CCs and their bandwidth sizes, and
estimating mobility of a UE reduces handover overheads and
risk of connection lost. In Section II-B, we have demonstrated
how to estimate data usage and mobility of each UE based on
user profile. It is important to note that the user profile can be
used with any existing CCs assignment methods.
B. User Profile Detection Based on Services
Historical data usage information of each UE plays crucial
role to identify user profiles. As shown in Table II, each UE
holds Times, Connection Time (Con. T) and Idle Time (Idle
T.), RT and NRT services data sizes for each eNB. In Table II,
Times illustrates how often a UE connects to eNBs, Con. T
represents how long a UE keeps connected eNBs and Idle T.
gives how long UE connected but not receive any services
from previous sessions for each band.
TABLE II
USER PROFILE DETECTION BASED ON ENODEBS
Band-a/Band-b/Band-c RT-Services NRT-Services
eNB-ID Times Con. T. Idle T. Video Game Web Mail
ID
1
f
1
c
1
t
1
v
1
g
1
w
1
m
1
ID
2
f
2
c
2
t
2
v
2
g
2
w
2
m
2
ID
3
f
3
c
3
t
3
v
3
g
3
w
3
m
3
ID
4
f
4
c
4
t
4
v
4
g
4
w
4
m
4
ID
5
f
5
c
5
t
5
v
5
g
5
w
5
m
5
ID
6
f
6
c
6
t
6
v
6
g
6
w
6
m
6
ID
7
f
7
c
7
t
7
v
7
g
7
w
7
m
7
ID
8
f
8
c
8
t
8
v
8
g
8
w
8
m
8
In order to identify user profile from Table II, some statical
analysis such as percentage measurement, can applied. For
example, percentage of Connection Time of UE i to eNB j
(C
i
j
) and percentage of Times of UE i to eNB j (T
i
j
) can
be simply calculated as follows:
C
i
j
100
c
j
k
°
s
1
c
s
and T
i
j
100
f
j
k
°
s
1
f
s
(1)
where k is the number of eNBs. Lower T
i
j
and higher C
i
j
indicate that UE i spends its more time around eNB j with
specified carrier band. On the other hand, higher T
i
j
and
lower C
i
j
indicate that UE i temporarily requests service
from eNB j. For example, UE i just uses eNB j while driving
home, to work or school.
Data usage of a UE can also be estimated from Table II.
For example, percentage of RT services for UE i in eNB j
can be simply measured as
RT
i
j
100
v
j
g
j
k
°
s
1
p
v
s
g
s
q
(2)
Like RT
i
j
, NRT
i
j
can be obtained. Furthermore, active
time percentage of UE i in eNB j (AT
i
j
) can be measured
as
AT
i
j
100
c
j
t
j
k
°
s
1
c
s
k
°
s
1
t
s
(3)
Similarly, data usage percentage of each service for any eNB
ID can be measured as above without classifying RT and NRT
services.
In addition to percentage analysis, average analysis can be
applied. For example, average connection time (ΘC
i
j
), average
RT (ΘRT
i
j
) and average NRT (ΘNRT
i
j
) data usage of UE i
in eNB j can be measured per connection as follows:
ΘC
i
j
c
j
f
j
, ΘRT
i
j
v
j
g
j
f
j
, ΘNRT
i
j
w
j
m
j
f
j
(4)
More average analysis can be used by an eNB to identify
a UE profile although no information is available for the eNB
in user profile table.
Eqns. (1) - (4) are some of examples which can be used
to identify user profiles based on Table II in order to provide
services which meet expectation of each UE.
III. CCS ASSIGNMENT METHOD FOR LTE\LTE-A
SYSTEM
Fig. 3 illustrates the proposed CCs assignment method in
LTE systems. Simply, the proposed method firstly finds the
number of required CCs and bands of CCs, and assigns them
to each UE. The proposed method considers three crucial
parameters that enable dynamic CCs assignment: (i) UE device
UE 1 UE 2
NRT Request
CCs for NRT
RT Request
CCs for RT
Band-a
LTE-A full capacity
Band-b
UE 3
LTE-A low\LTE
UE 4
Band-c
Fig. 3. Illustration of CCs assignment in LTE systems.
capacity in terms of LTE, LTE-A low capacity, and LTE-
A full capacity, (LTE-A low capacity should be considered
because multi-CCs assignment needs more memory and power
for processing [5]. Therefore, only one CC can be assigned
for LTE and LTE-A low capacity), (ii) data traffic types of
incoming requests (RT or NRT), and (iii) user profiles of UEs.
A. Number of Required CCs for Each UE
In order to estimate the number of required CCs for UE i
in eNB j, total and average data usage which obtained from
Table II are used. Therefore:
α
ΘRT
i
j
k
°
s
1
v
s
g
s
f
s
and β
ΘNRT
i
j
k
°
s
1
w
s
m
s
f
s
(5)
3

The number of required CCs for RT traffic (ηRT
i
j
) and NRT
traffic (ηNRT
i
j
) for UE i in eNB j can be obtained by using
α and β as follows:
ηRT
i
j
#
1
CC if
α
ξ
¤
1
α
ξ
CC if
α
ξ
¥
1 and
α
ξ
β
ξ
¤
5
(6)
and
ηNRT
i
j
#
1
CC if
β
ξ
¤
1
β
ξ
CC if
β
ξ
¥
1 and
α
ξ
β
ξ
¤
5
(7)
where ξ is the maximum data rate which a CC can carry for
active UEs. ξ can be calculated by considering modulations
and the number of UEs which are waiting for services in eNB
j. α
{
ξ
β
{
ξ
¤
5 because only five CCs will be aggregated in
LTE-A. If α
{
ξ
β
{
ξ
¡
5, CCs are divided for RT and NRT
services according to rate between ΘNRT
i
j
and ΘRT
i
j
.
B. CCs Assignment Process
By using above parameters, proposed CCs assignment
method process is as follows: (i) getting info about user
device capacity, (ii) finding all available CCs from resources,
(iii) measuring the number of UEs waiting for services to
find suitable CCs for each UE, (iv) reserving some CCs
with appropriate bandwidth sizes for NRT and RT services,
(v) measuring UE profile metrics by following procedure
in Sections II-B and III-A to determine the bands (whether
Band-a, Band-b, Band-c or multi bands) and estimating the
number of required CCs in each band, (vi) assigning the
number of required CCs which are determined based on user
profile to each UE (if there are more available CCs in specified
bands than the number of required CCs, one of CCs scheduling
algorithms such as R or LL can be used) and (vi) repeating
process in time intervals.
IV. SIMULATION OF THE SYSTEM
We have written discrete event simulation in Matlab by
taking into account the mentioned CCs assignment method
in Sections II and III.
A. Assumptions for eNBs
While implementing simulation, it is assumed that there is
only one eNB which has three bands to provide service to
UEs. The bands are divided as NRT and RT CCs. CCs for
NRT and RT services and their sizes and quantities are given
in Table III. The sizes and quantities are arranged based on the
800MHz, 1.8GHz and 2.6GHz. To reach required data rate for
TABLE III
NUMBER OF CCs WITH BANDWIDTH SIZE IN EACH BANDS
Band-a Band-b Band-c
Quantity Size Quantity Size Quantity Size
NRT x 10MHz 4 10MHz 4 10MHz
RT 5-x 10MHz 5 20MHz 4 20MHz
LTE systems, 10MHz bandwidth is chosen for NRT services
and 20MHz bandwidth is chosen for RT services from Band-b
and Band-c, and only 10MHz bandwidth is chosen for RT and
NRT services from Band-a because PCC is generally chosen
from a band which has higher range like Band-a. Therefore,
the bandwidth size of CCs is kept 10MHz for Band-a. In
addition, bandwidth size of NRT type CCs is 10MHz because
RT traffic data usage is more common than NRT data usage for
mobile devices. Size of NRT and RT packets is 512 bytes [22].
Therefore, NRT CCs can carry 10 packets and RT CCs can
carry 20 packets simultaneously by considering 25% percent
lower than CCs capacities because of bit and channels errors,
64QAM bit rate with normal cyclic prefix and 2 Physical
Downlink Control Channel (PDCCH) symbols.
B. Assumptions for UEs
There are three types of UEs, LTE, LTE-A low and LTE-A
full capacities in the system. 2/3 of UEs can only use one
CC but 1/3 of UEs can use multiple CCs. UEs are uniformly
distributed in area and UEs can use one or multi bands. 50%
of UEs can move around of the eNB for every iteration in
specified time interval. Each UE can only generate one type of
traffic (NRT or RT). Packet arrivals follow Poisson distribution
and arrival rates of traffic are getting higher when the number
of users is increased. Selected Transmission Time Interval
(TTI) for a packet is 1ms. CCs updating time for UEs is 10ms.
C. Observation Methodology
The simulation results in Section V are average of 1000
simulation runs for different UEs size. We observe the impact
of light and heavy UEs loads on CCs assignment procedure
mentioned in Section III-A by using Random CCs assignment
(R). R method is chosen for test cases because of simplicity.
There are three possible ways in order to see user profile CCs
assignment method effects on R method. They are: (i) how
only data usage estimation based on user profile affects R
method, (ii) how only mobility estimation based on user profile
affects R method, and (iii) how both data usage and mobility
estimation affect R method? In this report, only data usage
estimation based on user profile is used with simple mobility
estimation in order to show effects of the proposed method on
R method. In mobility estimation, just previously connected
bands are used without considering connection time (Cont. T.
in Table II)). Shortly, after finding the number of CCs for a UE
by estimating data usage, the number of CCs for the UE are
chosen from bands which were used previously by the same
UE if the UE is in the same or close to same position.
Random CCs assignment with the static number of CCs
(RSA), Random CCs assignment with the dynamic number of
CCs based on perfect user profile estimation (UPR), Random
CCs assignment with the dynamic number of CCs based on
user profile estimation for 10% error (UPR
10
) and Random
CCs assignment with the dynamic number of CCs based
on user profile estimation for 25% error (UPR
25
) have been
analyzed. User profile of each UE for UPR
10
and UPR
25
is
obtained by adding 10% and 25% errors, respectively, Error
means that data usage is estimated based on these above error
percentages. For example, a UE data usage rate is 100MB
4

0 50 100 150 200 250
UEs
0.0
0.2
0.4
0.6
0.8
1.0
Utilization
RSA
UP R
UP R
10
UP R
25
Fig. 4. Utilization of Band-a for RSA and
UPRs.
0 50 100 150 200 250
UEs
0.0
0.2
0.4
0.6
0.8
1.0
Utilization
RSA
UP R
UP R
10
UP R
25
Fig. 5. Utilization of Band-b for RSA and
UPRs.
0 50 100 150 200 250
UEs
0.0
0.2
0.4
0.6
0.8
1.0
Utilization
RSA
UP R
UP R
10
UP R
25
Fig. 6. Utilization of Band-c for RSA and
UPRs.
0 50 100 150 200 250
UEs
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
Throughput Rate
RSA
UP R
UP R
10
UP R
25
Fig. 7. NRT traffic throughput for RSA and
UPRs.
0 50 100 150 200 250
UEs
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
Throughput Rate
RSA
UP R
UP R
10
UP R
25
Fig. 8. RT traffic throughput for RSA and
UPRs.
0 50 100 150 200 250
UEs
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Fairness
RSA
L
UP R
L
UP R
10
L
UP R
25
L
RSA
F
UP R
F
UP R
10
F
UP R
25
F
Fig. 9. Fairness: Device base throughput rate
for RSA and UPRs.
but estimated data usage of the UE can be 125MB or 75MB
for UPR
25
and 110 or 90 for UPR
10
. Therefore, the proposed
method is evaluated under more realistic scenario.
D. Packet Scheduling
Without packet scheduling, the result cannot be obtained.
Therefore, we have used a simple packet scheduling method
in order to compare RSA and UPRs (UPRs represents UPR,
UPR
10
and UPR
25
together). Packet arrival traffics are kept
same for RSA and UPRs. UPRs dynamically arrange the
number of CCs based on user profiles and maximum possible
number of CCs is used for RSA. For test case, predetermined
static number of CCs is four for RSA because maximum
number of CCs for each UE is five in LTE systems and one of
them must be used for PCC (see Section II). Because of UEs
and eNB positions, CQI for all CCs is same for RSA and UPRs
during the simulation. RSA and UPRs transfer each packet
by using one of assigned CCs. If there are multiple packets
arrived from a UE, RSA and UPRs may transfer packets over
one or more of available CCs (without exceeding the number
of CCs) based on device types. If there are multiple available
CCs from different bands, firstly CCs which belongs to lower
range band (Band-c) are preferred to transfer the packet in
order to decrease traffic loads to higher range band (Band-a).
V. RESULTS
In this section, we present the performance of RSA and
UPRs by comparing utilization of bands, throughput of NRT
and RT traffics and fairness of service. Utilization of bands is
measured by dividing total packets of active users in each CC
to total capacity of CCs in each band then averaging the result
with total time steps (simulation time/10ms). Throughput rates
are measured by dividing transferred packets to all generated
packets for NRT and RT. Therefore, while increasing number
of UEs, throughput of traffic decreases for each UE. Fairness
of service is calculated based on throughput rate of UEs type in
order to see whether the service is provided fairly to all device
types. By these comparisons, resource usage and managed
QoS can be compared. The method which has lower resource
usage and higher throughput with equal fair service between
device types is better.
A. Utilization
Figs. 4, 5 and 6 show the utilization for Band-a, Band-b
and Band-c, respectively, obtained for RSA and UPRs. If
utilization of Band-a, Band-b and Band-c are compared, it
is observed that while the number of UEs is getting higher,
utilization of all bands is gradually increasing for all cases.
However, utilization of Band-a is increasing faster than uti-
lization of Band-b, utilization of Band-b is increasing faster
than utilization of Band-c for all cases. There are three reasons
for it: (i) bands which have higher range are used more than
bands which have lower range, (ii) distribution of UEs around
the eNB increases probability of lower amount of UEs located
in bands which have lower range and vice verse, and (iii) CCs
assignment based on R method without considering CCs loads.
Bands utilization results of UPRs are close to each other for
all bands while even error rate is increased to %10 and %25.
5

Citations
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Proceedings ArticleDOI
09 Mar 2015
TL;DR: This method will help service providers build efficient carrier components assignment methods through considering user profile and performance metrics, such as band usage, throughput and delay, to increase quality of services and experiences getting by mobile users.
Abstract: The number of mobile users is rapidly increasing. Therefore, the bandwidth demand of mobile users significantly growing. To answer users' demand, Carrier Aggregation is proposed in LTE systems. In Carrier Aggregation, communication between users and base stations are achieved by multi bands which have different coverage areas and mobile users can simultaneously connect one or multi carrier components in each band. Because of mobility of users and quality of carriers, the best available Carrier Components of each band should be assigned to each user in order to provide desired service to users. Several works have been proposed in the literature to address Carrier Components assignment methods in LTE systems by using Channel Quality Indicator, quality of service and service types. Although the previous works on carrier assignment methods significantly increase the performance of LTE system, continuously increasing bandwidth demand of users forces the operators to manage resource allocation more intelligently. Therefore, we have proposed a novel Carrier Component assignment method which considers user profiles and channel quality indicator to increase quality of services and experiences getting by mobile users. Results show that the proposed method uses system resources efficiently and can improve performance of LTE systems. Our method will help service providers build efficient carrier components assignment methods through considering user profile and performance metrics, such as band usage, throughput and delay.

9 citations


Cites background or methods from "Carrier components assignment metho..."

  • ...However, neither we have considered CQI and load balance properties for user profile carrier assignment method nor joint and partial carriers assignment techniques [4], [22] are investigated in user profile carrier assignment method....

    [...]

  • ...Finally, Section VI has the concluding remarks....

    [...]

  • ...Therefore, user profile, in addition to CQI, can be considered to increase QoS and Quality of Experience (QoE)....

    [...]

Proceedings ArticleDOI
30 Nov 2017
TL;DR: It can be concluded from the study that the CC selection algorithms for newly-arrived LTE users can benefit from the channel diversity and the load status whereas the carrier aggregation that does not allocate all of the available CCs to the newly arrived LTE-A users shown to be more efficient.
Abstract: Given that the demand for real-time multimedia contents that require significantly high data rate are getting of high popularity, a new mobile cellular technology known as Long term Evolution-Advanced (LTE-A) was standardized. The LTE-A is envisaged to support high peak data rate by aggregating more than one contiguous or non-contiguous Component Carriers (CCs) of the same or different frequency bandwidths. This paper provides a survey on the case where the LTE-A is working in backward compatible mode as well as when the system contains only LTE-A users. Note that the backward compatible mode indicates that the LTE-A contains a mixture of the legacy Long Term Evolution Release 8 (LTE) users that support packets (re)transmission on a single CC and the LTE-A users that are capable of utilizes more than one CCs for packets (re)transmission. It can be concluded from the study that the CC selection algorithms for newly-arrived LTE users can benefit from the channel diversity and the load status whereas the carrier aggregation that does not allocate all of the available CCs to the newly arrived LTE-A users shown to be more efficient.

2 citations


Cites background or result from "Carrier components assignment metho..."

  • ...In contrast to the previously stated work, the authors in [30] did not consider the channel quality instead decision on the number CCs of required by the newlyarrived LTE-A user was made on the basis of its profile (i....

    [...]

  • ...Algorithm in [30] • Determine the required number of CC based on user profile....

    [...]

Proceedings ArticleDOI
01 Dec 2014
TL;DR: Selective periodic component carrier assignment technique which allows continues data transfer during periodic carrier assignment operations is proposed and results show that the proposed technique increases throughput rate up to 25% and decreases average delay time up to 35%.
Abstract: Internet usage over mobile devices is on the rise. The bandwidth demand for mobile Internet access is also increasing with the number of mobile users. To answer users' demand, carrier aggregation is proposed in LTE-A system. In carrier aggregation, the best available one or more component carriers of each band are assigned to each user to provide efficient services. Several works have been reported in the literature on mandatory and periodic component carrier assignment methods. Although the previous works, especially periodic component carrier assignment methods, have significantly improved performance of LTE and LTE- A systems, many limitations still exist. One limitation of previous works is that data transfer is interrupted during periodic component carrier assignment operation which can decrease performance of the system. Therefore, in this paper, selective periodic component carrier assignment technique which allows continues data transfer during periodic carrier assignment operations is proposed. Results show that the proposed technique increases throughput rate up to 25% and decreases average delay time up to 35%.

1 citations


Cites methods from "Carrier components assignment metho..."

  • ...Here, the threshold can be dynamically arranged by using user profile information for each user as is done in our past work [13]....

    [...]

Journal ArticleDOI
TL;DR: Results indicate that the proposed selective technique increases the throughput ratio up to 18% and decreases average delay up to 50% and will assist service providers to build efficient periodic component carrier assignment methods to improve the performance of the system by reducing delay and increasing throughput ratio.
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TL;DR: This paper proves that the SU-MIMO (single-user MIMO) FDPS problem under the LTE requirement is NP-hard and therefore, it develops two approximation algorithms (one with full channel feedback and the other with partial channel feedback) with provable performance bounds.
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