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A Self-Adaptive Handoff Decision Algorithm for Densely Deployed
Closed-Group Femtocell Networks
Wahida Nasrin and Jiang Xie
Department of Electrical and Computer Engineering
The University of North Carolina at Charlotte
Email: {wnasrin, Linda.Xie}uncc.edu
Abstract—Due to the high traffic demand in cellular networks,
femtocells are considered as one promising solution for providing
cellular traffic offloading and better indoor coverage. However,
coexistence of femtocells with macrocell networks introduces
special challenges to mobility management. In particular, since
indoor and unplanned deployment of femtocells usually suffers
abrupt signal drop due to mutipath propagation, wall penetration
loss, and shadowing, unnecessary handoffs and ping-pong effects
may happen frequently, which severely degrades the quality of
connections and user experience. On the other hand, offloading
in femtocells requires a high cell utilization. Therefore, handoff
decision algorithms should be carefully designed to trigger proper
handoffs and fulfill the different requirements of macro-to-femto
and femto-to-macro handoffs. In this paper, we propose a location
history based adaptive handoff decision algorithm to address
the special challenges of indoor and unplanned deployment of
femtocells. Our proposed algorithm uses the neighboring cell
list in dense femtocell networks to obtain the location of users.
Based on the user location history, a new concept, handoff
frequency of occurrence, is introduced to assist intelligent handoff
decision-making. The hysteresis margin in our proposed handoff
decision criteria can be adaptively adjusted to meet various
handoff requirements. Simulation results show that our proposed
location history based adaptive handoff decision algorithm can
significantly improve the femtocell utilization and handoff failure
rate. To the best of our knowledge, this is the first adaptive
handoff decision algorithm that considers specific challenges of
indoor deployment of femtocells.
I. INTRODUCTION
The exponentially increased mobile data traffic is forcing
cellular networks to offload their traffic to other networks [1].
Indoor small cell deployment is one of the most promising
solutions to the seamless offloading of data from cellular net-
works. These low-powered, short-ranged, and low-cost indoor
small cells are known as femtocells [2], [3]. The support of
femtocells is a key feature of Long Term Evolution-Advanced
(LTE-A) system [4]. The deployment scenario of femtocells
in a LTE-A system is given in Fig. 1. Comparing with
macrocells, femtocells have the characteristic of unplanned
installation and management by users. The unplanned and
indoor deployment of femtocells within the existing macrocell
networks introduces a number of challenges. Spectrum allo-
cation, interference management, and mobility management
(MM) are the most important ones among them.
Handoff (HO) is an important operation to perform offload-
ing in femtocell networks [5]. How and when an offload is
This work was supported in part by the US National Science Foundation
(NSF) under Grant No. CNS-0953644, CNS-1218751, and CNS-1343355.
performed, and how long a user equipment (UE) can offload,
depend on the HO decision. There are two kinds of HO in
closed-access femtocell networks: macro-to-femto and femto-
to-macro. As only a limited number of subscribers, known
as closed subscriber group (CSG), have the access in closed-
group femtocells, they are more desirable as compared to
other access modes: open and hybrid [6]. In addition, the CSG
users want to be connected to femtocells as long as possible
while staying within the coverage area because of low-cost.
To meet this requirement, HO decision should work in a way
that macro-to-femto HO is early enough to offload from a
macrocell network, and femto-to-macro HO should wait to
trigger a HO until the signal strength from a femtocell goes
down. However, service failure may still happen if the HO-
decision cannot adapt with the unplanned nature of femtocell
networks. Due to the importance of HO decision in femtocell
networks, in this paper, we address HO decision related issues
for both macro-to-femto and femto-to-macro HO scenarios.
Fig. 1. Femtocell deployment scenario in an LTE-A system.
Though femtocells operate on the same frequency spectrum
as macrocells, the dense yet unplanned and indoor deployment
of femtocells within the overlaid macrocell networks makes
the HO decision more difficult and different from macrocell
networks. The first difficulty is the difference in the transmis-
sion power of a femto-base station (FBS). The transmission
power of a FBS (usually 10-15dB) is much lower than that
of a macro-base station (MBS) which is usually 45dB [7].
Because of this low transmission power, a femtocell might
be undiscovered by a UE. This can happen because a UE
has the natural tendency to connect to the highest received
signal strength (RSS) and it receives higher RSS from a
macrocell rather than a femtocell. There are two ways to
solve this problem. One is to take an offset to compensate
the power difference. This method is proposed in [8], [9] for
HO decision-making from macrocells to femtocells. Another
way is to set a proper threshold. In this paper, an optimization
of the HO threshold is proposed.
978-1-4673-7331-9/15/$31.00 c
2015 IEEE
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The second problem is related to co-channel interference.
Because of indoor deployment of femtocells, they suffer higher
path-loss due to multi-path propagation, wall penetration loss,
and shadowing effects than other small cells [3]. As a result,
an abrupt signal-to-interference-plus-noise-ratio (SINR) drop
occurs at the boundary of femtocells. Due to this drop, the
HO-decision algorithm for the femto-to-macro needs to be
different from that of the macro-to-femto. We will explain the
interference effect in femtocells in Section III and propose two
different HO-decision algorithms by considering the difference
between macro-to-femto and femto-to-macro HOs.
The unplanned and unstable nature of neighboring fem-
tocells introduces a third challenge. As femtocells are fully
operated by users and the network operator does not have
any control on this, the number and position of neighboring
femtocells may vary randomly. This nature of neighboring
femtocells will create an uneven interference effect on the
boundary of femtocells from time to time. As a result, it
is difficult to use the same HO-decision algorithm when the
environment changes occasionally, because this may cause a
large number of unnecessary HOs and even service failures.
Therefore, using an adaptive hysteresis margin (HM) is a good
way to solve this problem [10].
The use of an adaptive HM can reduce the number of
unnecessary HOs and ping-pong effects of users. An indoor
user follows a specific mobility pattern and moves back and
forth frequently in some boundary areas without actually
moving out of the cell (e.g., the corner of a room) and in
other boundary areas where the user actually moves out of the
cell (e.g., a door). This is the fourth obstacle when designing
the HO-decision algorithm. Using the same HM may lead to
unnecessary HOs and ping-pong effects in the former areas.
In this paper, we propose a self-adaptive HO-decision
algorithm to address the unique issues of both maro-to-femto
and femto-to-macro HOs. The HM of our algorithm is able
to adapt not only with the deployment environment, but also
with the mobility pattern of the user. The proposed algorithm
is intelligent enough to set the HM to a proper value based
on the history of previous HOs. We propose to keep a
database containing the location fingerprinting of a user who
has requested HOs before. The location fingerprinting is taken
from the measurements of the neighboring femtocells. The
goal of this self-adaptive HO-decision algorithm is to reduce
the rate of unnecessary HOs and service failure, and at the
same time, increase the cell utilization.
The rest of the paper is organized as follows. Related works
are explained in Section II. In Section III, research motivation
and contributions of this paper are discussed. In Section
IV, our location-fingerprint based self-adaptive HO-decision
algorithm is described. In Section V, simulation results are
given, followed by the conclusions in Section VI.
II. RELATED WORK
The problems addressed by existing research on HOs in
femtocell networks include transmission power difference
between MBS and FBS [8], [9], [11], [12], frequent and
unnecessary HOs [13]–[23], selecting the target cell for HOs
[16], [22]–[24], HO failure rate [14], [25], interference [12],
[14], [26], energy saving strategy [27], [28], HO delay/cost
minimization [14], [29], and ping-pong effects [30]–[32]. The
power difference between FBS and MBS during an inbound
(macro-to-femto) HO is considered in [8], [9], [11], [12]. In
[8] and [9], a combination factor is proposed to compensate
the power asymmetry in a way that the UE will be correctly
assigned to a femtocell while maintaining the number of HOs
at the same level. A window function is also proposed to
prevent the RSS from varying abruptly. However, the abrupt
signal drop cannot be ignored in real indoor scenarios. It
is claimed in [11] that only considering this combination
factor may increase the rate of unnecessary HOs. Therefore,
another parameter, transmission loss, is proposed in [11] for
HO decision-making. A cost-effective HO-decision algorithm
is proposed in [12] considering the power discrepancy which
will be discussed later.
Most of the existing works on HO decision in femtocell
networks are focused on minimizing the unnecessary HO
rate due to the dense femtocell deployment and small cell
radius [13]–[23]. A mobility prediction method to predict the
mobility pattern of a user, which is used to select the proper
cell to HO, is proposed in [13], [22], [23]. The mobility
prediction is based on the current mobility-history of a user.
Different parameters, such as user’s velocity, RSS, and traffic
type are considered for HO decision-making in [14]–[16].
In addition, call admission control (CAC) is used in [17]
and [19] for HO decision-making. A waiting time with a
SINR threshold is proposed in [18] to avoid unnecessary
HOs. Adaptive techniques to eliminate unnecessary HOs in
femtocell networks are considered in [20], [21]. An adaptive
HM is proposed based on the distance between a UE and
a BS to avoid unnecessary HOs in [20]. The efficiency of
two HO elimination techniques, i.e., windowing and HO delay
timer are investigated in [21]. Both techniques are modified for
femtocells based on the distance between a UE and the serving
BS. In conclusion, existing works on eliminating unnecessary
HOs consider user’s speed, traffic type, waiting time, mobility
pattern prediction, and distance-based adaptive HM for HO
decision-making.
Another problem of making a HO-decision in densely
deployed open-access femtocell networks is how to select the
target cell properly. Large number of femtocells may create a
long neighboring cell list and selecting a wrong target cell may
cause unnecessary HOs. To overcome this problem, mobility-
prediction is used to select a proper target cell [24] or to make
an effective neighboring cell list [16], [22]–[24]. [24] considers
that knowing the current position can help us know where a
UE is going, which can later help to select the target cell. As
described previously, [16] tries to avoid the long neighboring
cell list problem in order to eliminate unnecessary HOs by
considering user’s speed and traffic type. A mobility-history
database is proposed in [22], [23] which contains a list of
target cells where users are recently handed over.
A few works have addressed the interference problem
during a HO. Intracell HO (IHO) is considered in [12], [14],
[26] to avoid the cross-tier interference. A cost-function based
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on the available bandwidth of the target cell is proposed in
[14] to provide better QoS to users by reducing interference.
Along with these main issues, some other issues are also
addressed, such as to avoid the HO failure rate which is
one of the biggest challenges for designing a HO-decision
algorithm [14], [25], to minimize the HO cost [14], [29], and
to provide cost-effective service to users [33]. An intelligent
HO management is proposed in [27] for energy efficient
green femtocell networks and [28] works on reducing power
transmission at the UE side by adapting the HM suitably with
respect to the SINR from the target cell and the standard LTE
measurements.
The location-based HO-decision algorithm for different
small cell networks is discussed in [22], [23], [31], [32], [34],
[35]. As described earlier, [22] and [23] keep the mobility-
history of users to predict the target cell in small cell networks.
Here, location is used to set the target cell and to minimize the
HO delay. User’s mobility and location are also used to provide
better service during a HO. Geographical fingerprint for HOs
are considered in [36], [37], where location-fingerprint is
obtained using artificial neural networks. This fingerprint is
used to select the target cell and neither a GPS nor a sensor
is used.
Based on the discussion above, we observe that the follow-
ing issues in femtocell networks are not addressed for HO-
decision algorithms:
•Indoor environment
•Abrupt signal drop and high interference at cell boundary
•Indoor ping-pong effects
•Ad-hoc nature of neighboring femtocells
III. RESEARCH MOTIVATION AND CONTRIBUTIONS
A. Research Motivation
The above issues, not addressed in existing works, are the
motivation for our work. How and why these issues can affect
HO decision in femtocell networks are investigated in this
section.
Fig. 2. The comparison of SINR of different small cells.
Femtocells suffer from higher interference than other small
cells due to the indoor deployment. Simulation results on
the SINR with respect to the distance between a UE and
the BS of different small cells are shown in Fig. 2. We use
the ITU-R P.1238-7 path-loss model in our simulation [38].
The indoor deployment is indicated as NLOS (non-line-of-
sight) and outdoor deployment is indicated as LOS (line-of-
sight) here. From the figure, it is observed that a femtocell
suffers from higher interference than others because of the low
transmission power and indoor deployment. Hence, it has an
abrupt signal drop at the cell boundary. How this abrupt signal
drop and high interference affect the HO decision-making is
explained in Fig. 3.
Fig. 3. Effect of abrupt signal drop at the cell boundary in HO decision
making.
In the left side of Fig. 3, the comparison of the femtocell
RSS between indoor and outdoor deployment (which is in-
dicated as smallcell RSS) is shown. In the right side of Fig.
3, the scenario of selecting different HM is given. From the
figure we observe that, a HO should initiate early (at threshold
shown by the green dotted-line) because of the RSS drop at
the cell boundary. Now, as UE A moves towards the door, the
HM (HM1 in the figure) should be selected in a way that the
service of UE A does not fail. On the other hand, this HM
causes an unnecessary HO for UE B who will stay inside the
femtocell, which also leads to poor utilization of femtocells.
In this case, setting a high HM (HM2>HM1) is required
to overcome this problem. However, this is not acceptable for
UE A. To solve this conflicting issue, we need to design a
self-adaptive HO-decision algorithm.
Fig. 4. The effect of ad-hoc nature of neighboring femtocells on SINR at the
boundary of the home femtocell.
The changeable characteristics of both the home femtocell
and the neighboring femtocells make this situation more
complicated. Fig. 4 presents the SINR at the home femtocell
boundary with different number of neighboring femtocells. We
observe that the SINR changes randomly and unpredictably,
which can increase the ping-pong effects for indoor femtocell
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users. This issue needs to be addressed in order to provide
seamless mobility support between femtocells and macrocells
and to ensure a better user experience.
Besides all of these challenges, different HO scenarios in
femtocell networks have different criteria and purposes. They
are:
•Macro-to-femto HO: To offload traffic from macrocell
networks in order to avoid network congestion.
•Femto-to-macro HO: To provide seamless mobility man-
agement and better QoS while the indoor signal is poor
and the macrocell network is not congested.
To the best of our knowledge, all of these issues cannot be
addressed by existing works. The offset-based HO-decision
algorithms [8], [9], [11] can meet the requirement of macro-
to-femto HOs. However, they can increase the number of
unnecessary HOs for users. For example, in Fig. 5, all the UEs
will be handed over to the femtocell, which is not necessary.
Fig. 5. Inbound mobility in femtocell networks.
A number of existing works in the literature propose to
eliminate these redundant unnecessary macro-to-femto HOs.
Speed-based HO algorithms and cost-function based HO algo-
rithms [14]–[16] can eliminate unnecessary HOs of high-speed
users, which may also prevent the HO of closed-group users
with high-speed. As a result, offloading cannot be performed.
Moreover, these algorithms are based on some parameters
(e.g., speed, traffic types, and available bandwidth) which are
not practical for a UE to obtain or calculate. On the other hand,
the femto-to-macro HO scenario is different in that indoor
users are usually in low speed and they suffer high interference
at the cell boundary (which implies a high HO failure rate).
The necessity of a HO is different for different users based on
their locations (which is explained in Fig. 3). As a result, the
offset-based, speed-based, and cost-function based algorithms
cannot be applied to femto-to-macro HO scenarios. Existing
HO failure rate elimination algorithms [14], [25] use the same
parameters (speed, traffic type, etc.). They also cannot be
directly applied to femto-to-macro HO scenarios. Therefore,
more advanced HO decision algorithms are necessary to meet
these requirements. Existing adaptive HM-based algorithms
are either based on distance [10] or signal strength [20].
However, it is possible that users at the same distance to the BS
or with the same received signal strength may need different
HO decisions (as stated in Fig. 3). Considering all these issues,
we propose a self-adaptive HO-decision algorithm for both
macro-to-femto and femto-to-macro HO scenarios based on
the location-fingerprint of UE’s HO history. Existing works
related to location-fingerprint for HO decisions [36], [37] use
geographical fingerprint (i.e., latitude and longitude) to predict
the target cell. However, the target cell is always the same for
closed-group femtocell networks except that knowing the exact
location is hard for indoor users. Additionally, since we need
to find out only the HO areas, it is not necessary to know the
exact location of a UE. As a result, we use location-fingerprint
from the neighboring cell list and their RSS to find out the
HO area.
B. Contributions
In this paper, we propose a self-adaptive HO decision
algorithm based on location-fingerprint in order to provide
seamless HOs between macrocells and femtocells. The contri-
butions of this paper are summarized as follows:
•Considering the indoor deployment, we propose HO-
decision algorithms for both macro-to-femto and femto-
to-macro HO scenarios to meet offloading requirements
and to provide seamless mobility.
•We propose a self-adaptive HM to adapt to the ad-hoc
nature of femtocells and the locations of the UE. Each
time a HO is requested, a new HM is calculated from
a database entry based on the location-fingerprint of the
requested HO.
•We propose a location-fingerprint database to assist HO
decision-making. The database contains the information,
i.e., location-fingerprint of previous successful HOs. No
unrealistic data or measurement method is required to
build the database, as it contains the information of
neighboring cell IDs and their received signal strength
indicator (RSSI). Both parameters are accessible for a
UE during the HO measurement. To adjust with the ad-
hoc nature of both home and neighboring femtocells, we
propose to update the database each time a successful HO
happens.
•A realistic simulation scenario is used to evaluate the
performance of the proposed algorithm. We analyze the
proposed algorithm in terms of the rate of unnecessary
HOs, HO failure rate, and femtocell utilization, and
compare them with other existing works.
IV. THE PROPOSED SELF-ADAPTIVE HO DECISION
ALGORITHM
In this section, the proposed self-adaptive HO decision
algorithm for both macro-to-femto and femto-to-macro HOs is
introduced. The proposed algorithm works in two phases: 1)
initialization phase and 2) utilization phase. In the initialization
phase, a HO between a femtocell and a macrocell is triggered
using the LTE-A system-based HO criteria, and a database
is built in this phase. The database contains the location-
fingerprint of UEs that are successfully handed over to their
target cells. The database is used in the utilization phase
to adapt the HM for different UEs. During this phase, the
database is updated with new information to handle the ad-
hoc nature of femtocells. The notations used in our algorithm
are listed in Table I.
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TABLE I
NOTATIONS USED IN THE ALGORITHMS
RSSImReceived Signal Strength Indicator for macrocell
RSSIfReceived Signal Strength Indicator for femtocell
RSSImin Minimum received signal strength indicator for macrocell
Th Threshold for femtocell
HMmax Optimized value of hysteresis margin
MME Mobility management entity
FGW Femto gateway
PCI Physical cell identity
RSSIfail Minimum received signal strength indicator for femtocells
Fig. 6. Flow chart for the initialization phase.
Algorithm 1: Macro-to-femto HO-decision algorithm
if RSSIf>Thor RSSIm< RSSImin, and
RSSIf> RSSIm+HMmax then
HO to femtocell;
else
Stay in macrocell;
End;
Algorithm 2: Femto-to-macro HO-decision algorithm
if RSSIf<Thor RSSIm> RSSImin, and
RSSIf+HMmax <Th then
HO to macrocell;
else
Stay in femtocell;
End;
A. Initialization Phase
The initialization phase is activated at the time when a
new FBS is plugged in. The flow chart for this phase is
given in Fig. 6. The initialization phase is used to build up
the location-fingerprint database. In this phase, all HOs are
performed based on a fixed HM and the measurement report of
UEs. In LTE-A, the Radio Resource Control (RCC) protocol
manages the events that a UE reports its HO measurement
to the serving BS [39]. The measurement includes UE’s ID,
CSG ID, and available cell IDs (i.e., PCIs) along with their
RSSIs. The PCI is not a unique ID for FBS (totally 504 PCIs
from 0-503 are available for the LTE-A system). However,
we assume that there will be a good distribution of offered
PCIs within the coverage area of a macrocell. As shown in
the flow chart, this measurement is used to check whether the
UE is a registered-user for the closed-access femtocell. The
HO process continues for the closed-group users and the HO
decision. Whether the algorithm is in the initialization phase
or not is determined from the database. An empty database
indicates that the algorithm is in the initialization phase and the
serving cell makes the HO decision based on the measurement
report. The proposed HO algorithms for a macro-to-femto
HO and a femto-to-macro HO are given in Algorithm 1 and
Algorithm 2, respectively.
The selection of the Th and HMmax is explained later
in the paper. After a successful HO, the location-fingerprint
is entered in the database. We use both the neighboring cell
IDs and their RSSIs as the location-fingerprint of UEs. The
database has a specific length and the initialization phase ends
as soon as the database is full.
B. Utilization Phase
When the database is full, it will be used for determining the
adaptive HM in the utilization phase. Each time a user requests
a HO, it sends location-fingerprint with its measurement
report. After getting this report, MME (or FGW for femtocell)
checks the database to find matches. Suppose the number of
similar entries is Ndand the size of the database is ds. Then
the frequency of occurrence (Pfoc) can be found as
Pfoc =Nd
ds
.(1)
Pfoc is used for calculating the adaptive HM (HMad). A high
value of Pfoc indicates a frequent HO zone. As the frequent
HO zones need a lower HM, Pfoc and HMad are inversely
proportional to each other. Hence, the relationship between
them is HMad ∝1
Pfoc
or HMad ∝(1 −Pfoc)in dB. Given
HMmax , the HMad is
HMad =(1−Pfoc)∗HMmax.(2)
After calculating the value of the adaptive HM, the serving
BS checks the HO decision criteria. The proposed macro-to-
femto and femto-to-macro HO criteria are given in Algorithm
3 and Algorithm 4, respectively. The HO is successful if the
HO-decision criteria are met and the database is updated. The
steps of the utilization phase are shown in Fig. 7.
Algorithm 3: Macro-to-femto HO-decision algorithm
if RSSIf>Thor RSSIm< RSSImin, and
RSSIf> RSSIm+HMad then
HO to femtocell;
else
Stay in macrocell;
End;
Algorithm 4: Femto-to-macro HO-decision algorithm
if RSSIf<Thor RSSIm> RSSImin, and
RSSIf+HMad <Th then
HO to macrocell;
else
Stay in femtocell;
End;
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Fig. 7. Flow chart for the utilization phase.
Fig. 8. Database building and updating.
C. Location-Fingerprint Database
One of the important purposes of our proposed HO decision
algorithm is to get the location of the user during a HO,
because a HO is necessary at a few particular locations
for indoor users. However, localization of indoor users is
difficult. A number of localization techniques are available
in the literature for indoor localization [40]. Most of them
require complex algorithms. In our design, we consider RF
fingerprinting [41] instead of calculating the coordinates of
the user location, because our HO-decision algorithm does
not require the actual position of a user. Determining the
HO zone is our main purpose of building the database. If a
location-fingerprint, obtained from the neighboring cell list, is
stored in the database each time a HO occurs, the serving BS
can compare this list with the requested location-fingerprint,
and can perform a quick HO triggering when necessary. The
process of building the database is shown in Fig. 8.
Algorithm 5: Database building and update
if Data matches a previous entry within a time x from the same UE
then
Delete both entries;
else
if Database is full then
Delete the oldest data and insert a new entry;
else
Insert the data;
End;
Each time a UE sends a measurement report to the serving
BS, the serving BS determines the target BS and forwards the
rest of the measurement to the MME/FGW. This forwarded
message contains a list of neighboring cell IDs (with RSS I >
RSS Imin) and their corresponding RSSIs. The MME/FGW
stores this information in the database if the database is not
full. This database is used in the utilization phase to calculate
the adaptive values of the HM. To cope with the ad-hoc nature
of femtocells, in the utilization phase, the database is updated
in the FILO (first in last out) mode, i.e., the new location-
fingerprint is entered and the oldest data is removed from the
database if the database is full. The database building and
updating algorithm is given in Algorithm 5.
D. Determining HO Parameters
The minimum received signal strength at the cell boundary
of a macrocell and a femtocell is RSS Imin and RSSIfail,
respectively. To find out these values, Okumura-Hata propaga-
tion model is used for macrocell networks and ITU-R P.1238-7
path-loss model is used for femtocell networks [38].
Fig. 9. Selecting RSSImin at the macrocell boundary.
We consider the radius of macrocells and femtocells in our
simulation as 1.2km and 15m. Fig. 9 presents the RSSI values
for different distances from the macro BS. The RSSImin is
calculated as -75dB at the macrocell boundary as shown in
the figure. Similarly, the calculated RSSIfail value is -50dB
at the femtocell boundary which is shown in Fig. 10.
Fig. 10. Selecting RSSIfail and Th for a femtocell.
The value of HMmax and Th can be obtained using
simulations. We consider two contrary performance metrics
of femtocell networks: rate of unnecessary HOs and cell
utilization. The simulation results are shown in Fig. 11 with
respect to different values of Thresholds and HMs. From the
figure, it is observed that when HM =5dB and Th =−45dB,
both metrics show better performance than others. Therefore,
we set HMmax as 5dB. If the value of HMmax is 5dB in
Fig. 10, we can also find that Th =−45dB. These values are
used our simulation.
E. HO Signaling
Both the inbound and outbound signaling for self-adaptive
HO decision in femtocell networks are given in Fig. 12 and
Fig. 13.
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(a) Rate of unnecessary HOs (b) Cell Utilization
Fig. 11. Rate of unnecessary HOs and cell utilization for different Th and
HM.
We consider the signaling procedure during a macro-to-
femto HO as the inbound signaling. In this state, the MME
checks the location-fingerprint database for similar entries
after getting the measurement report from the UE through the
serving BS. If the database is empty, the MME considers that
the initialization phase is activated and makes a HO decision.
The location-fingerprint is added to the database after the HO
is succeeded. In the utilization phase, this database is used to
calculate the HM as described previously and the database is
updated with new location-fingerprint. The database is shared
by the MME and FGW so that it can be used for both inbound
and outbound mobility. In the outbound signaling, i.e., in a
femto-to-macro HO, the signaling procedure is similar to the
inbound signaling. However, the FGW makes the HO-decision
instead of the MME. The outbound signaling is shown in Fig.
13.
Fig. 12. Inbound signaling for self-adaptive HO-decision algorithm.
V. P ERFORMANCE EVALUATION
In this section, we evaluate the performance of the proposed
self-adaptive HO-decision algorithm. We use Net Logo 5.0.5
[42] to simulate our proposed algorithm in an indoor envi-
ronment. We design a single-floored two-bedroom apartment
with an FBS which has the capacity of supporting fifteen
users surrounded by six neighboring FBSs in the coverage
of a macro BS. We consider thirty users with a probability
of 0.5 to enter and exit the apartment in a random manner.
All users and FBSs are placed randomly and all users follow
a modified version of the Random Waypoint mobility model.
The mobility model is modified in a way that the users only
use the door to get in/out of the apartment and none of them
crosses the walls. For supporting the unplanned deployment of
femtocells, we also consider the random placement of FBSs
inside the apartment. The parameters used in our simulation
are listed in Table II [38].
Fig. 13. Outbound signaling for self-adaptive HO-decision algorithm.
TABLE II
SIMULATION PARAMETERS
Macrocell transmission power, Pm45 dB
Radius of macrocell 1.2 km
Femtocell transmission power, Pf10 dB
Radius of femtocell 15 m
Size of database, ds30
Users speed 5 km/hr
Threshold, Th -45 dB
Wall penetration loss 5dB
Outdoor penetration loss 2dB-10dB
RSSImin -75 dB
RSSIfail -50 dB
HMmax 5dB
In this paper, we mainly investigate the following three
performance metrics: 1) Rate of unnecessary HOs: the proba-
bility that a UE temporarily hands over to the target cell and
hands over back to the serving cell, 2) HO failure rate: the
probability of a call/service-drop before a successful HO is
triggered, and 3) cell utilization: the probability that a CSG
UE stays connected to the femtocell while within the coverage
area of it’s home FBS. In addition, we compare our proposed
self-adaptive algorithm with three other algorithms: 1) fixed
HM AL: the HM does not adapt with the ad-hoc nature of
femtocells; 2) AL1: the HM changes based on the formula from
[10], which is HM =max{HMmax ∗(1 −10 d
R)4;0}. Here,
Ris the radius of the femtocell and dis the distance between
the FBS and UE; and 3) AL2: the adaptive HM is calculated
from HM =max{HMmax ∗(1 −10
SINRact−SI NRmin
SINRmin−SI NRmax )4;0}
[20].
A. Rate of Unnecessary HOs
The rate of unnecessary HOs for the macro-to-femto HO,
femto-to-macro HO, and both of them together are shown
in Fig. 14, Fig. 15, and Fig. 16, respectively. Low rate
of unnecessary HOs is desirable in order to provide better
performance. The simulation result shows that the proposed
2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)
396
algorithm has a lower unnecessary HO rate than other algo-
rithms. As AL1 and AL2 change the HM based on the distance
and SINR, respectively, and select the minimum value of the
HM throughout the cell boundary, both algorithms show worse
performance than others. Unlike the proposed algorithm, all
the other three algorithms fail to adapt the HM based on
the HO location area. As a result, the proposed algorithm
eliminates more unnecessary HOs than others.
Fig. 14. Rate of unnecessary HOs for the macro-to-femto HO scenario.
Fig. 15. Rate of unnecessary HOs for the femto-to-macro HO scenario.
Fig. 16. Total rate of unnecessary HOs.
B. HO Failure Rate
The HO failure rate should be as minimum as possible.
Since a high value of the HM can lead to a high value of
the HO failure rate because of the abrupt signal drop, it is
necessary to minimize the HM where a HO is necessary.
Additionally, femtocells suffer high interference at the cell
boundary, which may lead to a high service failure if HO-
decision cannot adapt to the change of the environment. If the
RSSI of the UE goes below RSSIfail and a HO does not
happen, we consider this as a HO failure. The performance of
the HO failure rate of our proposed self-adaptive algorithm as
compared to the other three algorithms is given in Fig. 17. It
is observed that the proposed algorithm outperforms the other
algorithms in terms of lower HO failure rate.
Fig. 17. HO failure rate.
C. Cell Utilization
As femtocells are deployed for offloading cellular traffic and
to provide cost-effective service to the closed-group users, it is
expected that whenever a UE is within the coverage area of its
home FBS, it should be connected to the femtocell. However,
traditional HO-decision algorithms do not consider this issue.
As a result, their cell utilization is lower than the proposed
algorithm. The simulation results of cell utilization are shown
in Fig. 18.
Fig. 18. Cell utilization.
VI. CONCLUSION
In this paper, a location-fingerprint based handoff decision
algorithm is proposed to improve the handoff performance
and to offload cellular data traffic in densely deployed het-
erogeneous networks with femtocells. In our algorithm, the
hysteresis margin changes with the handoff priority based on
the location of users. Therefore, a fast handoff can be triggered
wherever necessary. Our algorithm can reduce the handoff
failure rate and at the same time, provide better cell utilization
to insure maximum data offloading. The performance of the
proposed self-adaptive handoff-decision algorithm is analyzed
in terms of unnecessary handoff rate, handoff failure rate,
and cell utilization by considering the challenges of indoor
deployment. Simulation results show significant improvement
2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)
397
as compared to the existing handoff-decision algorithms in
femtocell networks. It is observed that a proper selection
of hysteresis margin and threshold can reduce unnecessary
handoff rate and handoff failure rate without sacrificing cell
utilization.
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