In the second scenario, we reduce the difference in
background traffic between high-load CFAPs and lowload CFAPs in order to evaluate the efficiency of the
MPCE-based scheme when CFAPs have almost similar high
traffic loads. The simulation results observed in Figure 6
shows that, in comparison with the performance shown in
Figure 3, these studied schemes provided similar performance in term of handover number. The reason is that
the Prediction-based scheme and the RSS-based scheme
only decide the femtocell selection according to the RSS
value. Therefore, changing the background traffic load does
not make any difference to these schemes. In case of the
Sensing-based and the MPCE-based schemes, changes of
the background traffic will cause only little changes to the
performance in term of handover number because these
schemes also use the RSS value when creating the list of
tentative target CFAPs by using Equations (8) and (10),
respectively.
Figure 7 shows that since the background traffic load of
CFAPs was similarly high, the downlink packet delay of
all four schemes increases. We observe that when CFAPs
had nearly similar background traffic loads, the difference
in performance of these femtocell selection schemes was
reduced. However, we still can observe that the MPCEbased scheme provided lower packet delay than other
schemes. The reason is that the MPCE-based scheme can
help select and maintain a stable connection with the CFAP
that has more available channel bandwidth. Performance
results in Figure 8 show that the difference in throughput
of all schemes was reduced. However, the MPCE-based
scheme still can provide higher throughput comparing to
others schemes. The performance results of the simulation
for Scenario 2 are reasonable because, theoretically, when
all femtocells have high traffic load, handover performance
will be worse since there are less available radio resources
for handover connections.
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Research and Development on Information and Communication Technology
Femtocell Selection Scheme for Reducing
Unnecessary Handover and Enhancing Down-
Link QoS in Cognitive Femtocell Networks
Nhu-Dong Hoang1, Nam-Hoang Nguyen2, Trong-Minh Hoang3 and Takahiko Saba4
1 Viettel R&D Institute, Hanoi, Vietnam
2 University of Engineering and Technology, Vietnam National University Hanoi, Vietnam
3 Post and Telecommunications Institute of Technology, Hanoi, Vietnam
4 Chiba Institute of Technology, Chiba, Japan
E-mail: donghoang93@gmail.com, hoangnn@vnu.edu.vn, hoangtrongminh@yahoo.com, saba@cs.it-chiba.ac.jp
Correspondence: Nam-Hoang Nguyen
Communication: received 24 July 2017, revised 7 August 2017, accepted 30 August 2017
Abstract: Femtocell networks have been proposed for
indoor communications as the extension of cellular networks
for enhancing coverage performance. Because femtocells have
small coverage radius, typically from 15 to 30 meters, a
femtocell user (FU) walking at low speed can still make
several femtocell-to-femtocell handovers during its connection.
When performing a femtocell-to-femtocell handover, femtocell
selection used to select the target handover femtocell has to
be able not only to reduce unnecessary handovers and but
also to support FU’s quality of service (QoS). In the paper,
we propose a femtocell selection scheme for femtocell-to-
femtocell handover, named Mobility Prediction and Capacity
Estimation based scheme (MPCE-based scheme), which has
the advantages of the mobility prediction and femtocell’s
available capacity estimation methods. Performance results
obtained by computer simulation show that the proposed
MPCE-based scheme can reduce unnecessary femtocell-to-
femtocell handovers, maintain low data delay and improve
the throughput of femtocell users.
Keywords: Cognitive radio, femtocell selection, femtocell han-
dover, Quality of Service (QoS).
I. INTRODUCTION
The evolution of wireless communications technologies
and mobile devices brought up many advantages to mobile
users. It leads to the unimaginable growth of the number
of mobile users and the amount of data delivered in mobile
networks [1]. To fulfill the requirements, cognitive radio
and femtocell are considered as the key technologies which
are expected to build cognitive femtocell networks for the
future 5th generation (5G) mobile communications [2–4].
Although femtocell networks are mainly deployed for in-
door communications in small areas, a femtocell user (FU)
might still have to perform several femtocell-to-femtocell
handovers during its connection lifetime because femto-
cells have small coverage radius and high density [5, 6].
Femtocell selection is an important function of femtocell-
to-femtocell handover which has to find an accurate target
femtocell. An efficient femtocell selection scheme should
be able to reduce the number of unnecessary handovers
and avoid overloading femtocells. We can find a number
of femtocell selection methods in literature such as [7–10]
which commonly use mobility prediction or signal strength
for selecting a target femtocell. However, to our best
knowledge, the problems of unnecessary handovers and
FU’s QoS support are still open challenging research issues.
In this paper, we first discuss a generic system model
of cognitive cellular femtocell networks. We then describe
briefly the operation of three femtocell selection schemes of
interest. The first one is conventional and based on Received
Signal Strength (RSS), hence denoted here as RSS-based
scheme. The second one is designed based on mobility
prediction, hence denoted as Prediction-based scheme. The
third one is designed based on downlink capacity estima-
tion, hence denoted as Sensing-based scheme. The latter
two schemes have been introduced before in our previous
paper [11]. Extended from this work, we propose in this
paper a fourth scheme, which is based on both Mobility
Prediction and Capacity Estimation (MPCE), hence denoted
as MPCE-based scheme. This scheme takes the advantages
of mobility prediction and femtocell’s available capacity
estimation methods. Its performance is evaluated and com-
pared to those of the other three schemes.
The paper is organized as follows. The system model
is described in Section II. The conventional RSS-based,
Prediction-based, Sensing-based and MPCE-based femto-
45
Research and Development on Information and Communication Technology
Figure 1. Cognitive femtocell network model.
cell selection schemes are presented in Section III. Simu-
lation model and parameters are described in Section IV.
Performance evaluation and comparison are presented and
discussed in Section V. Conclusions are given in Section VI.
II. SYSTEM MODEL
The generic system model of cognitive femtocell net-
works is illustrated in Figure 1 which was first introduced
in [12]. In this model, Femtocell Management System
(FMS) and Mobile RAN Management System (MRMS)
have periodical information exchanges to support mobility
management and radio resource management. When a
femtocell user (FU) moves from one femtocell zone to
another or between a femtocell zone to and a MBS zone,
the FU needs support of connection handover. We carry
out research of femtocell-to-femtocell handover in practical
scenarios that Cognitive Femtocell Access Points (CFAPs)
are deployed with a high density (high building residential
areas, shopping centers, airports, railway stations, etc.).
Assume that the downlink channel uses dynamic time
division multiplexing, i.e., FUs can be assigned variable
downlink time slots according to the data amount to be sent
from the serving CFAP. CFAPs have cognitive functional-
ities including spectrum sensing, which allow them to be
able to measure and sense the downlink transmission occu-
pancy of CFAPs nearby [13]. By sensing the occupancy of
downlink channel of neighbor CFAPs, a CFAP can analyze
the estimated available capacity of the neighbor CFAPs.
The information can be considered as a useful criterion
when a serving CFAP wants to choose a target CFAP for
FU’s handover. A FU needs a handover when the handover
condition is triggered, that is,
10 log10
XCFAP(i)(t)
XservingCFAP(t) ≥ handover threshold, (1)
where CFAP(i) is a neighbor CFAP of the serving CFAP,
XCFAP(i)(t) and XservingCFAP(t) are the pilot signal strength
sent from a neighbor CFAP(i) and the serving CFAP
measured at a FU at the time t, respectively.
III. FEMTOCELL SELECTION SCHEMES
In this section, we first describe the operation of
three other femtocell selection schemes including the
conventional RSS-based scheme, Prediction-based scheme
and Sensing-based scheme. We analyze disadvantages of
these schemes and then present the proposed MPCE-
based scheme which can eliminate existing problems of
other schemes.
1. RSS-based Scheme
The RSS-based femtocell selection scheme uses the
strength of the received signal as the criterion for the
serving CFAP to select the target CFAP for FU’s handover.
When a FU has an active connection, it periodically sends
a report of RSS measurements of neighbor CFAPs to its
serving CFAP.
When the handover condition in (1) is triggered, accord-
ing to the measurement report of the FU, the serving CFAP
will select the target CFAP which satisfies this condition
and has the highest RSS among the neighbor CFAPs.
That is,
XtargetCFAP (t) = max{XCFAP(i)(t) |
CFAP(i) ∈ neighbor CFAPs, XCFAP(i)(t) satisfies (1)}.
(2)
By selecting the target femtocell which has the strongest
RSS, the RSS-based scheme can provide the high quality
wireless link to the FU. However, this scheme does not
guarantee whether the target femtocell has available capac-
ity or not. It is not able to reduce the unnecessary handovers
which happen when the FU has a short residing time in the
target femtocell.
2. Prediction-based Scheme
When a FU moves into the overlapping areas of CFAPs,
the variation of RSS can cause unnecessary handovers
which will increase the signaling overhead and reduce the
system performance. A handover prediction for femtocell
wireless networks has been proposed in [10], which re-
lies on the distance-based prediction and computationally
expensive algorithm in order to optimize the selection
of target femtocells. In our previous research [11], we
proposed the Prediction-based scheme that aims to avoid in-
effective handovers while consuming low computing load.
46
Vol. E–3, No. 14, Sep. 2017
This scheme applies the exponential smoothing theory for
predicting demand [14] to combine the relation of the
RSS information collected in the past with the current
RSS information in order to reduce the variation of the
received RSS value and predict the mobility trend of the
FU. The scheme operates as follows. The FU measures the
RSSs of neighbor CFAPs and reports to its serving CFAP
periodically. Using the RSS information report, the serving
CFAP will estimate the average relative RSS value X(t) of
a neighbor CFAP as
X(t) = αX(t) + (1 − α)X(t − 1), (3)
and the average mobility trend as
b(t) = α(X(t) − X(t − 1)) + (1 − α)b(t − 1), (4)
where X(t) represents the actual RSS value at time t, X(t)
is the estimated average relative RSS values at time t, b(t)
is the average mobility trend which is used to evaluate and
predict how the relative RSS value will vary, and α is the
weighted value to evaluate how the current values and past
values affect the average relative value. The higher value
of b(t) corresponding to a CFAP, the higher the probability
that a mobile FU will come across. By calculating and
considering different values of α, we observed that the
most suitable value of α should be in the middle of the
range from 0 to 1. We select α = 0.5 for the performance
evaluation later.
When the handover condition of (1) is triggered, with
X(t) corresponding to the average relative RSS value at
time t, the serving CFAP generates a set A of CFAPs whose
estimated average relative RSS values satisfy this condition.
That is,
A = {CFAPi | i ≥ 1, Xi(t) satisfy (1)}. (5)
The serving CFAP selects the target CFAP in A that has
the highest average mobility trend, by
btargetCFAP (t) = max{bCFAP(i)(t) | CFAP(i) ∈ A}. (6)
3. Sensing-based Scheme
The Prediction-based scheme was designed to reduce
unnecessary handovers but it does not consider the QoS
provision of FUs. The target CFAP should have available
channel capacity for provisioning QoS to arriving FUs.
As the downlink channel deploys dynamic time division
multiplexing, if the channel has more free time slots, it
can provide lower packet delay and higher throughput to
arriving FUs. This inspiration led us to propose the Sensing-
based scheme in [11], which was designed based on the
assumption that a CFAP can use its cognitive functionality
to sense free time slots of the channel of neighbor CFAPs
in order to estimate the available channel capacity for
arriving FUs. A serving CFAP will evaluate the idle level of
neighbor CFAPs during every sensing cycle period of one
second. The idle level is called as Free Time Ratio (FTR),
which is defined as the ratio of the amount of free-time
in a sensing cycle over a sensing cycle time. The amount
of free-time of a neighbor CFAP during a sensing cycle is
defined as the total time that its downlink channel is free,
that is,
FTR =
Free-time in one sensing cycle
Sensing cycle period
. (7)
When the handover condition of (1) is triggered, with X(t)
corresponding to the RSS value at time i, the serving CFAP
generates and maintains a set B of target CFAPs whose RSS
values satisfy this condition. That is,
B = {CFAPi | i ≥ 1, XCFAP(i)(t) satisfy (1)}. (8)
The serving CFAP selects the target CFAP in B that has
the highest FTR, by
FTRtargetCFAP = max{FTRCFAP(i) | CFAP(i) ∈ B}. (9)
4. MPCE-based Scheme
In the Prediction-based scheme, we were concerned
about how to reduce the unnecessary handover frequency,
while in the Sensing-based scheme, we focused on selecting
the target CFAP which has high available channel capacity.
Naturally, it is of our interest to develop a more efficient
femtocell selection scheme that can take into account
of the advantages of both mentioned femtocell selection
schemes, that is, reducing unnecessary handover frequency
and enhancing QoS metric in terms of packet delay and
throughput. In particular, we propose in this section the
MPCE-based femtocell selection scheme which combines
the effectiveness of Prediction-based and Sensing-based
schemes. When performing the MPCE-based scheme, the
serving CFAP uses the mobility prediction technique as
described in the Prediction-based scheme to create a set of
tentative target CFAPs from neighbor CFAPs. The serving
CFAP uses the cognitive functionality to calculate the FTR
of the neighbor CFAPs.
When the handover condition of (1) is triggered, with
X(t) corresponding to the average relative RSS value at
time t, the serving CFAP creates a set C of CFAPs whose
estimated average RSS values satisfy this condition. That is,
C = {CFAPi | i ≥ 1, Xi(t) satisfy (1)}. (10)
The serving CFAP selects the CFAP in C that has the
highest FTR, by
FTRtargetCFAP = max{FTRCFAP(i) | CFAP(i) ∈ C}. (11)
47
Research and Development on Information and Communication Technology
10 20 30 40 50 60 70 80 90 100 110
10
20
30
40
50
60
70
80
Low load CFAP High load CFAP
Figure 2. Simulation model.
IV. SIMULATION MODEL AND PARAMETERS
The simulation model is shown in Figure 2. “Low-
load” and “high-load” CFAPs have different load ratios
of downlink data connections. Each CFAP has the cov-
erage radius of 15 m and the antenna height is in range
between 1 m and 5 m. In each CFAP coverage area, FUs
are uniformly distributed and have the antenna height of
1.5 m. Considering the case in which CFAPs and FUs are
indoor devices, standardized path-loss models and common
simulation parameters are given in Table I.
Except left-edge CFAPs, other CFAPs generate back-
ground traffic according to their load ratio, which is the
ratio of the total amount of generated downlink background
data in a CFAP to the downlink bandwidth (see Table II).
The left-edge CFAPs generate only mobile FUs every 50 s
and create their downlink connections. Having been cre-
ated, the mobile FUs will move to the right side in random
directions. During their movement, handovers will occur.
Each mobile FU has connection holding time following the
exponential distribution with mean of 180 s. If a mobile FU
reaches the right-edge or when its connection holding time
expires, its number of handovers is updated. Two simulation
scenarios and their parameters are shown in Table II.
V. PERFORMANCE COMPARISON
For performance comparison, we evaluate and compare
the cumulative distribution function (CDF) of the num-
ber of handovers per connection, packet delay and FU’s
throughput. In general, the simulation results indicate that
the proposed MPCE-based scheme has better performance
and satisfies all requirements of low unnecessary handover,
low packet delay and high user throughput. More detailed
discussion about the performance is given below.
TABLE I
SIMULATION PARAMETER
Parameters Values
Indoor to indoor path loss model ITU P.1238 [15]
Frequency 850 MHz [16]
External wall loss 10 dB [16]
Window loss 5 dB [16]
Speed of user 0.5 m/s
Indoor to indoor lognormal
shadowing standard deviation
4 dB [16]
Downlink bandwidth 10 Mbps
Time slot duration 0.1 ms
TABLE II
SIMULATION SCENARIOS
Simulation
scenario
Parameter Load ratio (%) of
background traffic
Scenario 1
Low-load CFAP 40
High-load CFAP 80
Scenario 2
Low-load CFAP 60
High-load CFAP 80
The simulation results observed in the first simulation
scenario are shown in Figures 3, 4 and 5. Figure 3 shows
that the Prediction-based scheme and the MPCE-based
scheme were able to reduce the unnecessary handover fre-
quency. The Prediction-based scheme is the most effective
scheme in terms of providing low number of handovers
because it gives the highest selection priority to the target
CFAP where FUs can reside for long time. Because the
MPCE-based scheme attempts to satisfy the unnecessary
handovers, provides low packet delay and improves the
throughput, it can offer better performance of handover
number than the RSS-based and Sensing-based schemes.
When we consider the ability to reduce the downlink
packet delay, it can be seen in Figure 4 that the MPCE-
based scheme outperformed other schemes. The Prediction-
based scheme and RSS-based scheme cause high packet
delay because they are not able to select the target CFAP
which has available bandwidth. When using the Prediction-
based scheme, if the target CFAP is a high-load CFAP,
the Prediction-based scheme decides to handover FUs to
a high-load CFAP. That will lead to an increase of packet
delay when FUs transmit data after handover. Considering
the throughput of mobile FUs, the performance results in
Figure 5 show that the MPCE-based and Sensing-based
schemes performed better than the two remain schemes.
That means using MPCE-based and Sensing-based schemes
can satisfy both low packet delay and high throughput.
In contrast to the Prediction-based scheme, the Sensing-
based scheme can help the serving CFAP to avoid selecting
48
Vol. E–3, No. 14, Sep. 2017
Handover number per connection
P
(ha
nd
o v
er
nu
m
be
r≤
ha
nd
ov
er
nu
m
be
r(i
))
Figure 3. CDF of handover number per connection in Scenario 1.
Packet delay (s)
P
(pa
c k
et
de
la
y
≤
de
la
y(i
))
Figure 4. CDF of packet delay in Scenario 1.
throughput (Mbps)
P
(th
ro
ug
hp
ut
≤
th
ro
ug
hp
ut
(i)
)
Figure 5. CDF of throughput in Scenario 1.
a high load CFAP as the target CFAP for FU. However,
the variation of the RSS increases the handover number
in the Sensing-based scheme. That increases the number
of unnecessary handovers and, therefore, the Sensing-based
scheme provides higher packet delay than the MPCE-based
scheme, as shown in Figure 4.
Handover number per connection
P
(ha
nd
o v
er
nu
m
be
r≤
ha
nd
ov
er
nu
m
be
r(i
))
Figure 6. CDF of handover number per connection in Scenario 2.
Packet delay (s)
P
(pa
ck
et
de
la
y
≤
de
la
y(i
))
Figure 7. CDF of packet delay in Scenario 2.
throughput (Mbps)
P
(th
ro
ug
hp
ut
≤
th
ro
ug
hp
ut
(i)
)
Figure 8. CDF of throughput in Scenario 2.
In the second scenario, we reduce the difference in
background traffic between high-load CFAPs and low-
load CFAPs in order to evaluate the efficiency of the
MPCE-based scheme when CFAPs have almost similar high
traffic loads. The simulation results observed in Figure 6
shows that, in comparison with the performance shown in
49
Research and Development on Information and Communication Technology
Figure 3, these studied schemes provided similar perfor-
mance in term of handover number. The reason is that
the Prediction-based scheme and the RSS-based scheme
only decide the femtocell selection according to the RSS
value. Therefore, changing the background traffic load does
not make any difference to these schemes. In case of the
Sensing-based and the MPCE-based schemes, changes of
the background traffic will cause only little changes to the
performance in term of handover number because these
schemes also use the RSS value when creating the list of
tentative target CFAPs by using Equations (8) and (10),
respectively.
Figure 7 shows that since the background traffic load of
CFAPs was similarly high, the downlink packet delay of
all four schemes increases. We observe that when CFAPs
had nearly similar background traffic loads, the difference
in performance of these femtocell selection schemes was
reduced. However, we still can observe that the MPCE-
based scheme provided lower packet delay than other
schemes. The reason is that the MPCE-based scheme can
help select and maintain a stable connection with the CFAP
that has more available channel bandwidth. Performance
results in Figure 8 show that the difference in throughput
of all schemes was reduced. However, the MPCE-based
scheme still can provide higher throughput comparing to
others schemes. The performance results of the simulation
for Scenario 2 are reasonable because, theoretically, when
all femtocells have high traffic load, handover performance
will be worse since there are less available radio resources
for handover connections.
VI. CONCLUSIONS
In this paper, we have presented challenging research
issues of femtocell-to-femtocell handover in a practical
system model of cognitive femtocell networks where fem-
tocells are deployed with a high density. Reducing unnec-
essary handovers and supporting QoS of femtocell users
are most important requirements of the cognitive femto-
cell networks. In order to fulfill the challenging require-
ments, we proposed a new MPCE-based femtocell selection
scheme, which aims to eliminate unnecessary handover
and provide low packet delay and high throughput to
mobile femtocell users. This scheme exploits advantages
of mobility prediction and femtocell’s available capacity
estimation methods. We have compared the performance
of the proposed MPCE-based scheme with other femtocell
selection schemes in several scenarios where femtocells are
densely deployed. The simulation results obtained by com-
puter simulation verified that the proposed MPCE-based
scheme can achieve better performance than the other three
schemes did, providing a lower number of handover per
connection, lower packet delay and higher femtocell user
throughput. Future works include the investigation of other
open research challenges such as mobility management
for group mobility, femtocell-to-macrocell and macrocell-
tofemtocell handover scenarios.
ACKNOWLEDGMENT
This work was supported by VNU University of Engi-
neering and Technology and Chiba Institute of Technology.
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[14] R. G. Brown, “Exponential smoothing for predicting
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Nhu-Dong Hoang received the Bachelor
Degree in Electronics and Telecommuni-
cations (2015) from University of Engi-
neering and Technology, Vietnam National
University Hanoi. He is currently a research
engineer of Viettel R&D Institute, Vietnam.
Nam-Hoang Nguyen received the B.Eng
and M.Eng in electronics and telecommuni-
cations from Hanoi University of Technol-
ogy in 1995 and 1997, respectively and the
PhD degree of electrical engineering from
Vienna University of Technology in 2002.
He is currently the lecturer of University
of Engineering and Technology, Vietnam
National University Hanoi. His research interests include wireless
communications, visible light communications and next genera-
tion mobile networks.
Trong-Minh Hoang received the Mas-
ter degree in electronics and telecommu-
nication engineering (2003), and the PhD
degree in telecommunication engineering
(2014) from Posts and Telecommunications
Institute of Technology (PTIT). He is cur-
rently a lecturer in the telecommunications
department of PTIT. His research interests
include QoS and security for multi-hop wireless communication
networks; mathematical analysis to model and analyze behavior
of complex systems.
Takahiko Saba received his B.E., M.E.,
and Ph.D. degrees all in electrical engineer-
ing from Keio University, Yokohama, Japan
in 1992, 1994, and 1997, respectively. From
1994 to 1997, he was a Special Researcher
of Fellowships of the Japan Society for the
Promotion of Science for Japanese Junior
Scientists. From 1997 to 1998, he was with
the Department of Electrical and Computer Engineering, Nagoya
Institute of Technology, Nagoya, Japan, as a Research Assistant.
From 1998, he joined the Department of Computer Science,
Chiba Institute of Technology, Narashino, Japan, where is now
a Professor. From 2015, he is a Vice President of Chiba Institute
of Technology. He is a member of IEEE, and a fellow of IEICE,
Japan. He is currently a Chair of Technical Affairs Committee,
Asia-Pacific Region, IEEE Communications Society, and a Chair
of Editorial Board of IEICE Communications Society. His current
research interests include wireless communications and physical
layer security.
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Các file đính kèm theo tài liệu này:
- femtocell_selection_scheme_for_reducing_unnecessary_handover.pdf