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Energy Efficient Handover Algorithm For Green
Radio Networks
G. Araniti+, J. Cosmas∗, A. Iera+, A. Molinaro+, A. Orsino+, P. Scopelliti+
+ARTS Lab., DIIES Dep., University Mediterranea of Reggio Calabria, Italy
∗WNCC, Brunel University, London, UK
email: {araniti, antonio.iera, antonella.molinaro, antonino.orsino}@unirc.it,
john.cosmas@brunel.ac.uk
Abstract—The coverage area and the capacity of existing
cellular network systems are not sufficient to meet the growing
demand of high data rate for wireless communications. Het-
erogeneous Networks (HetNets) based on Long Term Evolution
(LTE) can be a possible solution to enhance indoor coverage,
deliver high bandwidths and off-load traffic from the macro base
stations. However, this technology is still under development and
several open issues have to be still investigated, such as interfer-
ence coordination, power consumption, resources management
and handover techniques. The aim of this work is to guarantee
the reduction of power consumption using a new handover
algorithm based on green policy. In addition, the proposed
scheme guarantees the minimization of unnecessary handovers.
The simulation campaigns have been conducted through the
open-source Network Simulator 3 (NS-3).The preliminary results
demonstrate that an efficient use of green approach improves the
HetNETs performance in terms of power saving, energy efficiency
and allow to reduces the number of unnecessary handovers
Index Terms—HetNet; 4G mobile communication; Green Han-
dover; Power Saving; Green Networks; Networking and QoS;
Performance evaluation.
I. INT ROD UC TI ON
THE fast diffusion of advanced user terminals and multi-
media applications such as mobile web-browsing, video
downloading, on-line gaming, and social networking, leads to
the growing demand of high data rate for wireless commu-
nications. In addition, the lack of radio spectrum due to the
more and more request of bandwidth by the end users in order
to achieve high transmission rates and high levels of Quality
of Service (QoS) represents still an open issue.
Since the existing cellular networks are not able to solve
this problem, a new paradigm called Long Term Evolution
(LTE) has been proposed as the basis for the fourth generation
mobile cellular networks (4G) [1]. The aims of the LTE
standard are higher user bit rates, lower delays, increased
spectrum efficiency, reduced cost, and operational simplicity.
However, the presence of coverage holes and weak signal areas
due to the cell edge and high level of inter-cell interference,
leads the scientific community to consider new paradigms
and approaches [2], [3]. A possible enhancement of the LTE
systems is to overlap different low power base stations (BSs)
within an existing macro cellular coverage. Such a system is
called Heterogeneous Network (HetNet) [4].
LTE HetNet can be a possible solution to enhance indoor
coverage, delivers high bandwidths and off-loads traffic from
the macro base stations. The high power BSs (i.e. macrocells)
are integrated with low power BSs (i.e.: pico, femtocell, and
relay nodes) that are dynamically arranged and turned on/off
directly by the end users. Although the usage of small BSs
enhances the coverage of the system and the network perfor-
mance, the inter-cell interference increases linearly and has to
be taken into account. In addition, the use of a high number of
small cells causes a high power consumption and increases the
inter-cell interference. Power consumption, power saving, and
energy efficiency are the main topics of the Green Networks
[5] [6] [7]. The aim of these networks is to design power
control schemes and Radio Resource Management (RRM)
algorithms in order to guarantee the power saving and the
energy reduction of the networks without decrease the users
and system performance. These goals can be achieved by
minimizing the base station energy consumption through the
implementation of energy efficient hardware, power saving
protocols, and switching-off the BSs through efficient han-
dover procedures.
Frequent and unnecessary handover by the users to low
power BSs are a serious problem for the system performance.
Indeed, they cause a high overhead due to the exchange of
signalling messages with a consequent decreasing of system
performance. Three different kinds of handover can be exe-
cuted: (i) Outbound (femto →macro), (ii) Inbound (macro
→femto) and (iii) inter-Femto-Access-Point(FAP) (femto →
femto) [8]. Both Inbound and Inter-FAP handover are quite
complex since there are hundreds of possible target FAPs. Vice
versa the Outbound handover is more simple, because there is
only one macrocell target in the considered coverage area.
In this paper, we propose a new handover algorithm based
on green policies in order to guarantee an efficient man-
agement of the BSs transmitted power and to reduce the
unnecessary handovers. The proposed algorithm rejects the
Inbound handover requests from the users with high mobility
and allows only the handovers that do not increase the overall
transmitted power of the BS target. It will be demonstrated
that the proposed scheme increases the power saving and
minimizes the number of unnecessary handovers.
The remainder of the paper is organized as follows. In
Section II we briefly discuss the related work of the handover
procedures in LTE HetNets. In Section III we introduce the
proposed green handover algorithm, whereas the simulation
campaign and the achieved results are given in Section IV.
Finally, conclusive remarks and future works can be found in
Section V.
II. RE LATE D WOR K
Conventional handover schemes do not assure an optimal
management of the handover procedures over the HetNets and
the handoff from Macrocell to femtocell is still an open issue.
UEs need to select the appropriate target femtocell among
many candidates by taking into account the interference level,
UE speed and the available resources of the target cell. Power
consumption is one of the most important problems affecting
new generation systems. In fact, most of energy consumption
of the telecommunication networks is caused by the base
stations.
Since there are several femtocells within a macrocell area,
the femtocell deployment increases the energy consumption.
The 3GPP TS 36.927 (release 10) [9] identifies as potential
solutions for energy saving (ES) three alternatives: (i) the
totally switch-off of the base stations when there are not users
,(ii) the trigger of the ES procedures in case of light traffic,
and (iii) the use of the femtocells in ”idle” mode.
Ashraf et al. in [10] proposed to improve the energy
efficiency of femtocells via the user activity detection. The
proposed procedure allows the femtocell to switch-off the ra-
dio transmissions in presence of no active calls involved. This
method, however, does not foresees an effective procedure to
reduce the ping-pong effect. Therefore, the total power con-
sumption increases if idle femtocells makes the wrong decision
to ”wake up” in order to execute an unnecessary handover.
In [11] a scheme for unnecessary handover minimization is
presented. Authors proposed a Call Admission Control (CAC)
technique to improve the handover process under particular
conditions. Three parameters are taken into account: (i) the
Received Signal Strength (RSS), (ii) the time in which a
Mobile Station (MS) maintains the minimum required signal
level, and (iii) the Signal-to-Interference Noise Ratio (SINR).
The handover requests are triggered if the SINR from the
femtocell is greater than the SINR from the macro and if the
RSS from the femtocell is greater than a given threshold.
In [12] authors proposed a new handover algorithm based
on the UEs speed and the QoS requirements. They consider a
dense femtocell scenario where users with high mobility cross
the femtocell coverage in a short time. Under these conditions,
the authors consider that users with high speed do not need
to make a handover, in particular when they support non-real-
time services. Three different environments are analyzed: (i)
low mobile state (from 0 to 15 km/h), (ii) medium mobile
state (from 15 to 30 km/h) and (iii) high mobile state (above
30 km/h). In addition, they consider real-time and non-real-
time traffics in the simulation campaigns for the evaluation
of the proposed algorithm. Differently from [12], an handover
decision policy based on mobility prediction is proposed in
[13] by considering as maximum speed 10 Km/h. A reactive
and proactive handover strategy is also proposed to mitigate
the frequent and unnecessary handover.
In [14] the authors proposed a new handover procedure
between macrocell and femtocell based on the use of the UEs
residence time in a cell and by exploiting two different thresh-
olds for the serving and the target cell, respectively. Authors
demonstrated that such an approach allows to reduces the
number of unnecessary handovers. Two different thresholds
are exploited
In [15] authors developed a green handover protocol in
two-tier OFDMA networks (macrocell and femtocell). This
is mainly based on the prediction of the dwell time (tdwell )
and average expected transmission time (texpected) of the UE.
The algorithm consists of three phases: (i) free spectrum
configuration, (ii) transmission time estimation, and (iii) green
handover decision. In order to improve the energy efficiency of
the network, the handover framework proposed in [15] wake-
up periodically the BSs from the idle mode. In this way, they
have a timely response to the network changes.
III. THE GR EE N ALG OR IT HM
Much of the power consumption takes place in the base
stations. In addition, in a high density femtocell deployment,
the signaling overhead due to frequent handovers between
macrocell and femtocells causes the decrease of network
performance. Following these considerations, we propose a
handover algorithm based on green policies in order to guar-
antee an efficient management of the transmitted power of the
base stations and the reduction of the unnecessary handover
procedures.
Fig. 1: Algorithm Flowchart
The algorithm is composed of two parts. In the first part,
a CAC technique rejects handover requests from macrocell to
femtocell of the users with high mobility. In the second part,
a green power control scheme named Green Handover takes
into account the average SINR of the femtocell. Since a weak
SINR causes the increase of transmitted power, only users that
not decrease the performance in term of average SINR of the
UEs of the femtocell are allowed to hand-in in the cell.
The algorithm proposed in this paper starts with the collec-
tion of the UE measurements of the downlink channel (i.e.:
CQI, SINR, SNR, RSRQ, RSRP) in order to evaluate if a
handover procedure has to be performed. As mentioned in
Fig. 2: Adopted scenario
the introduction, three different kinds of handovers can be
executed: (i) Outbound, (ii) Inbound and (iii) inter- FAP. In
the bound case, the handover is always accepted without any
constraint. In the last two cases, a Call Admission Control
(CAC) technique based on the user speed is considered as
shown in the Fig. 1 according to the following three different
situation:
1) if the UE speed is faster than 10 km/h (vehicular user)
handover is rejected.
2) if the UE speed is below than 5 km/h (pedestrian user)
the handover is executed.
3) if the UE speed is in the range 5, 10 km/h, the following
green approach is executed in order to guarantee the
power saving.
In the proposed green approach, the base station target
accepts the new user only if it does not increase the intra-cell
interference. Furthermore, the base station assigns the power
and the appropriate modulation and coding scheme (MCS)
in order to guarantee the minimum SI N Rmin related to the
CQI forwarded every Transmission Time Interval (TTI). In
this phase, in order to select the most suitable SI N Rmin we
use the mapping reported in Table 1.
CQI Modulation Code Rate SINR SE
CQI 1 QPSK 1/12 -6.50 0.15
CQI 2 QPSK 1/9 -4.00 0.23
CQI 3 QPSK 1/6 -2.60 0.38
CQI 4 QPSK 1/3 -1.00 0.60
CQI 5 QPSK 1/2 1.00 0.88
CQI 6 QPSK 3/5 3.00 1.18
CQI 7 16QAM 1/3 6.60 1.48
CQI 8 16QAM 1/2 10.00 1.91
CQI 9 16QAM 3/5 11.40 2.41
CQI 10 64QAM 1/2 11.80 2.73
CQI 11 64QAM 1/2 13.00 3.32
CQI 12 64QAM 3/5 13.80 3.90
CQI 13 64QAM 3/4 15.60 4.52
CQI 14 64QAM 5/6 16.80 5.12
CQI 15 64QAM 11/12 17.60 5.55
TABLE I: CQI values [16]
The choice to exploit the minimum SI N Rmin instead of
the actual SI N Ractual 1for each handover user allows to
reduce the base station transmitted power. Indeed, after that
the minimum level of the SINR is selected, the power gain
(PG) for each user within the BS coverage is calculated as
follow:
PG=SI N Ractual −SINRmin [W att](1)
Then, the new transmitted power of the BS is evaluated by
taking into consideration the following equation:
PBSnew =PBS −
nUE
X
k=1
PG(k) [W att](2)
where PnUE
k=1 PG(k)is the sum of all the power gain of the
users within a BS and PBS in the transmitted power.
IV. SIM UL ATION RES ULT S
Performance evaluation of the proposed algorithms have
been conducted through the usage of the Lena module of
the well-know Network Simulator 3 (NS-3) [17]. NS-3 is
used because is capable of carrying out large-scale network
simulations in an efficient way. In addition, it is able to emulate
and simulate the entire LTE protocol stack and the most used
wireless telecommunication standard.
In order to evaluate the system performance of the proposed
algorithm, we consider an LTE HetNets scenario with a dense
deployment of femtocells within the macro cellular coverage.
It is worth nothing that high and low power nodes have
different transmission powers. In particular, we consider a
transmitted power equal to 46 dBm and 20 dBm for macrocell
and femtocell, respectively. Users are uniformly distributed
with different speeds and the total bandwidth (i.e., 20 MHz)
is equally divided with 50 RBs2for the macrocell and 50 RBs
for all the femtocells. The mobility of the users and their speed
varies in a randomly from 3 m/s to 20 m/s. Since the users
are free to move in all direction all three type of handover
(outbound, inbound, and intra-FAP) are considered.
Outputs have been achieved by averaging a sufficient
number of simulation results in order to guarantee a 95%
confidence interval. The Path Loss models, different for macro
and femtocell, exploited during the simulation campaign are
reported below:
P Lmacro = 15.3 + 37.6 log10 R[dB](3)
P Lfemtocell = 38.46 + 20 log10 R+Lw[dB](4)
where Ris the distance between the transmitter (BS) and
the receiver (UE) in meters and Lwis the wall penetration
loss of the wall separating apartments. In order to evaluate
the system performance, the LTE throughput and the energy
1it is worth noting that the SINRactual is the real SINR estimated by the
users every TTI.
2The RB corresponds to the smallest time frequency resource that can be
allocated to a user (12 sub-carriers) in an Long Term Evolution (LTE) system.
For example, a channel bandwidth of 20Mhz corresponds to 100 RB.
efficiency achieved by the proposed algorithms are computed
as:
T=PN
i=1 T BRBi
T T I [Kbps](5)
EE =T h
Pt
[bits
J oule ](6)
where T BRBiis the transport block size referred to the
ith RB, and N is the total number of RBs available in the
system, while TTI is the scheduling time. As specified in 6,
EE represents the ratio between the overall amount of bits
received respect the total power consumption of the system.
The further main system parameters are summarized in
Table 2:
Parameter Macro-cell Femtocell
1-st sub-channel fre-
quency
2110MHz
Downlink Bandwidth 10MHz
Sub-Carrier
Bandwidth
15kHz
Doppler Frequency 60Hz
Resource block band-
width
180kHz
Resource block carri-
ers
12
Resource block
OFDM symbols
7
BS downlink TX
power
30dBm 8dBm
Noise spectral density −174dB m/Hz
Pathloss (distance R) P Lmacro = 15.6 + (35 ·log(R))dB
P Lfemto = 38.46 + (20 ·log(R))dB
Shadow fading log −normal,ϑ = 8 dB
Wall penetration loss 10dB 7,10,15dB
Frame duration 10ms
TTI (sub-frame dura-
tion)
1ms
Target Bit Error Rate 5×10−5
Cell coverage 500m50m
BS distance 400m
UE 1,2,5,10,15,20,25,30
TABLE II: Main system parameters
Fig. 3 shows the overall transmitted power of the femtocell
in the system. The power increases proportionally with the
number of low power nodes and the proposed green algorithm
introduces a gain in average equal to 55% when the traffic load
of the system is high (20 users within the femtocell). As we
expected, the amount of total power consumption is always
lower introducing the green handover algorithm. In addition,
the figure shows that the gap increases with the number of
cells.
In any case, in all analyzed scenarios we have a considerable
gain with respect to the no green case due to the low power of
the femtocell that is in the order of 0.1 Watt. Therefore, greater
is the number of the low power nodes into the macro cellular
coverage and better are the performance of the algorithm. Best
5 10 15 20 25 30
0
0.5
1
1.5
2
2.5
3
3.5
Number of Femtocells
Total Power Femtocells [Watt]
NoGreen
Green 1UE
Green 10UE
Green 20UE
Fig. 3: Femtocells Total Power
results are obtained in the configuration with 30 femtocells and
20 users per femto and appreciable power saving is achieved
with 10 users too.
5 10 15 20 25 30
0
50
100
150
200
250
300
350
Number of Femtocells
System Throughput [Mbps]
NoGreen 1UE
Green 1UE
NoGreen 10UE
Green 10UE
NoGreen 20UE
Green 20UE
Fig. 4: Total Throughput of the system
The average system throughput by considering the green
and no green case in shown in Fig. 4. The gap with the no
green case increase with the number of the femtocells and the
users of the system. In fact, with a low-density scenario the
trend by considering both cases (green and no green)is the
same and it means that our algorithm enhances the system
power consumption without decreases the BS performance.
Fig. 5 shows the average throughput per UE by considering
three different number of femto-users and by varying the
number of the femtocells per macrocell. The performance
decrease proportionally with the number of the low power
nodes. Moreover, the behavior of the proposed handover
algorithm is similar to the non-green case. In this case, the
performance seem to be better with few users inside the cell.
But it is an expected result. The performance reduction in
terms of average UE throughput is mainly due to the use of
SI N Rmin , in particular when the number of the system users
is high.
The energy efficiency achieved by the system is shown in
Fig. 6. Good performance are obtained when the number of
the femtocells is low. It is due to the low performance achieved
by the system. In scenario with dense number of low power
nodes and low number of users the proposed algorithm does
not introduce any significant gain.
The transmitted power of the base stations play an important
role to guarantee an efficient usage of the system energy.
Furthermore, in Fig. 6 differently by the No-Green case, with
the proposed algorithm the energy efficiency is constant by
5 10 15 20 25 30
0
5
10
15
20
25
30
Number of Femtocells
UE Throughput [Mbps]
NoGreen 1UE
Green 1UE
NoGreen 10UE
Green 10UE
NoGreen 20UE
Green 20UE
Fig. 5: Average UE Throughput
5 10 15 20 25 30
0
50
100
150
200
250
300
Number of Femtocells
Energy Efficiency [Mbps / Watt]
NoGreen 1UE
Green 1UE
NoGreen 10UE
Green 10UE
NoGreen 20UE
Green 20UE
Fig. 6: System Energy Efficiency
increasing the number of users and femtocells.
Finally, the overall handover performed in the system are
shown in Fig. 7. For sake of simplicity, only the case with
10UEs is shown due to the fact that the other cases (1UE and
20 UEs) have the same trend. The proposed algorithm is able
to reduce the unnecessary and frequent handovers that cause
the decreasing of system performance due to the overhead
generated by signaling.
1 2 5 10 20 30
0
20
40
60
80
100
120
140
Number of Femtocells
Accepted Handover
NoGreen 10UE
Green 10UE
Fig. 7: Overall Handover: Green vs No-Green
It is clear that increasing the number of femto increases the
amount of handovers, especially without the green handover
algorithm. Greater is the deployment ratio of the femtocells
and bigger is the probability that a UE passes through the
overlapping area. As shown In Fig. 7, in all cases the number
of handovers executed increases with the increasing of femto-
cells deployed either using the proposed algorithm either not.
V. CO NC LU SI ON A ND FU TU RE WO RK S
In this work is investigated a possible solution to improve
the handover performance in LTE-based HetNets. A handover
algorithm based on green policies is proposed in order to
efficiently manage handover procedure guaranteeing power
saving. In addition, the algorithm allows the reduction of
unnecessary and frequent handovers.
The obtained results show that the performance achieved
by our algorithm increase linearly with the number of the
femtocells and the system users. In particular, a considerable
gain in term of power consumption at cost of low system
performance loss is reached. Respect to the no green case,
it is shown that the introduction of green policies allows
the decreasing of the system power consumption and, as a
consequence, unnecessary handover procedures are avoided.
A possible future extension of this work could takes into
consideration the effect of green policies in high power nodes.
An extended scenario with more than one macrocell and dif-
ferent types of low power nodes (such as picocells, microcells
and relate nodes) could be evaluated in order to understand the
impact of power saving schemes in the handover procedures.
Finally, the proposed algorithm could be used in different
networks standard and paradigm such as the Vehicular Ad-hoc
NETwork (VANET) and machine type communication such
as Machine-to-Machine (M2M) and Device-to-Device (D2D)
paradigms.
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