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Wireless Pers CommunDOI 10.1007/s11277-015-2333-2
An Intelligent Hand-off Algorithm to Enhance Qualityof Service
in High Altitude Platforms Using NeuralNetwork
S. H. Alsamhi N. S. Rajput
Springer Science+Business Media New York 2015
Abstract Efficient hand-off algorithm enhances the capacity and
quality of service (QoS) ofcellular systems. Hand-off algorithm is
used in wireless cellular systems to decide when andto which base
station (BS) will receive the handoff call, without any service
interruption. Highaltitude platforms (HAPs) is considered as a
complementary BS to mobiles in an obstacleposition. HAPs can supply
services to uncovered areas of terrestrial systems, thus with
thegoodness of HAPs total capacity in a service-limited area will
be improved. Recently, artificialneural network (ANN) has been
utilized to improve hand-off algorithms due to its abilityto handle
large data. As a revolutionary wireless system, ANN helps in taking
the hand-offdecision based on receive signal strength, speed,
traffic intensity, and directivity. Radial basedfunction network is
used for making a hand-off decision to the chosen neighbor BS.
Thispaper presents novel approaches of combining HAPs and
terrestrial system in a particularcoverage area for the design of
high performance hand-off algorithm. It is found that hand-offrate
and blocking rate are greatly improved using ANN for handoff
decision.
Keywords High altitude platforms (HAPs) Radial base function
network (RBFN) Hand-off algorithm Artificial neural network
(ANN)
1 Introduction
Cellular communications provides communication facility to
mobile subscribers (MSs). Aservice area is divided into a number of
cells [1]. Several such cells constitute a cluster. The
S. H. Alsamhi (B) N. S. RajputDepartment of Electronics
Engineering, Indian Institute of Technology (Banaras Hindu
University),Varanasi, UP, Indiae-mail:
[email protected]
N. S. Rajpute-mail: [email protected]. H. AlsamhiIBB
University, Ibb, Yemen
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available frequency spectrum is used in each cluster. Each cell
in a cluster uses a fractionof the available channels in the
spectrum allocated according to a channel assignment strat-egy and
is served by base station (BS). Hand-off is a common technique
employed by allcellular systems, both terrestrial and satellite,
which has been proven vital both for ensuringuninterrupted
connections and increasing system capacity [2,3].
A hand-off is the process of transferring a mobile stations
serving BS from one to anotherwhen the mobile station moves across
the cell boundary. A properly designed hand-offalgorithm is
essential in reducing the switching load of the system while
maintaining thedesired QoS of the call in progress and a low
probability of blocking new calls. The hand-off process determines
the spectral efficiency and the quality perceived by users [4].
Forenhancing the capacity an efficient hand-off algorithms is
required.
HAPs is airplane or airship that operates at altitude 1721 km
[5]. It provides line of sight,better channel condition as well as
high coverage area [6]. The speed of wind is sufficient lowin HAPs
position. Coexistence of HAPs and terrestrial systems using
spectrum etiquettes isinvestigated [7]. The coverage area of HAPs
is divided into three zone that are urban areacoverage (UAC),
suburban area coverage (SAC) and rural area coverage (RAC)
[6,8].
In this paper we are assuming that the platform of HAPs will be
moved in vertical andhorizontal which will effect on the coverage
area and Hand-off process. It is possible to employa combination of
hand-off techniques and a steering mechanism, to avoid
interruptions onthe link between the user and the platform. To do
so, either the customer premises equipment(CPE) should be keep
track of the platform and / or the HAPs itself should employ an
antennasteering mechanism to maintain a constant coverage. HAPs is
proposed as a complementaryBS to mobiles in an obstacle position as
shown in Fig. 1. HAPs can supply services to themobile having weak
signal from the serving terrestrial BS influenced by shadowing,
turningcorner as well as being outside the terrestrial
coverage.
Recently, ANN have been applied to many diverse problems. Neural
network is trained topredict a users transfer probabilities [9]. To
achieve an efficient handoff, ANN is explored.ANN helps in taking
the handoff decision based on receive signal strength (RSS),
bandwidth,delay etc. Combination of these parameters, then carry on
training. After training ANN iscapable of taking appreciate and
efficient hand-off decision.
The rest of this paper is organized as follows. In Sects. 2 and
3, classification of hand-offand desirable features of hand-off
have been described, respectively. The HAPs movementhas been
described in Sect. 4. In Sect. 5, neural network algorithm has been
carried out andthe results have been shown in Sect. 6.
2 Classification of Hand-off
The hand-off process determines the maximum number of calls that
can be served in a givenarea [10]. Figure 2 shows a simple hand-off
scenario in which an MSs travels from BS-Ato BS-B. Initially, the
MSs are connected to BS-A. The overlap between the two cells is
thehand-off region in which the mobile may be connected to either
BS-A or BS-B. At a certaintime during the travel, the mobile is
handed-off from BS-A to BS-B, When the MS is closeto BS-B.
Hand-off classification can be classified in several ways [11]
depend on type of type ofnetwork, number of connection and entity
as shown in Fig. 3. In first type, type of net-work represents as
horizontal and vertical handoff, hard and soft handoff,
mobile-controlled,mobile-assisted, and network-controlled
handoff.
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Fig. 1 Concept of HAP cellar
Fig. 2 Hand-off cellular system
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S. H. Alsamhi, N. S. Rajput
Fig. 3 Hand-off classification
In Horizontal occurs when the MSs move between different BS of
the same network. Onthe other hand, vertical handoff occurs when
handoff is required between different wirelessnetworks. Second type
is number of connection which represents by hard and soft.
Hardhandoff, the MS must break its connection from the current
access network before it canconnect to a new one. But the MS can
communicate and connect with more than one accessnetwork during the
handoff process in case of a soft handoff. Third type is depends
onentity and represents by mobile-controlled, mobile-assisted, and
network-controlled handoff,mobile-assisted handoff is the hybrid of
mobile-controlled and network-controlled handoffwhere the MS makes
the handoff decisions in cooperation with the access network.
3 Desirable Features of Hand-off
A seamless hand-off is typically characterized by two
performance requirements [12]:
a. The hand-off latency should be no more than a few hundreds of
milliseconds.b. The QoS provided by the source and target access
networks should be nearly identical
in order to sustain the same communication experience.
Figure 4 describes several desirable features of hand-off
algorithms as mentioned in theliterature [13]. Some of these
features are described below:
1. Hand-off should be fast enough to avoid service
degradation.2. Hand-off should be reliable such that the MSs will
be able to maintain the required QoS
after hand-off.3. Successful handoffs to total attempted
handoffs should be maximized.4. Number of Hand-off: the number of
hand-off must be minimized.5. The effect of handoff on QoS should
be minimal.6. The handoff latency should be low.
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Fig. 4 Desirable hand-off features
4 HAPs Movement
Really, the position of HAPs is not fixed and will vary with
time dependent on the prevailingwinding conditions in the
stratosphere. Investigation of the potential use of phased
arraytechnology was done in [14] to cope with platform movement.
When the platform is moving,it would also be necessary to
compensate motion by electronic or mechanical means in orderto keep
the cells stationary, or to hand-off connections between cells as
is done in cellulartelephony.
4.1 Vertical Shifting
HAPs comprises of individual antennas for each cells on the
ground which is fixed in relationto each other as shown in Fig. 5.
Thus the coverage area on the ground has a fixed subtendedangle.
The coverage area of HAPs can be calculated by the following
formula [15]:
A ={
[(h + h1) tan ]2 (h tan )2 for upward vertical shift [(h h2) tan
]2 (h tan )2 for downward vertical shift (1)
where, A is the coverage area, h is the altitude, h1 and h2 is
the change in upward anddownward height respectively, is subtended
angle which is fixed.
4.2 Horizontal Shifting
HAPs movement can change position or distort the shape of the
individual cells. In the case ofthe HAPs drifts from the center of
the coverage area, cells move from their intended position.The
coverage will be increase in the direction of the platform and the
user in opposite directionwill lose coverage as shown in Fig. 6.
The approximate coverage as the following:
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Fig. 5 Vertical shifting up and down of HAPs
Fig. 6 Horizontal shifting of HAPs
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An Intelligent Hand-off Algorithm
Fig. 7 Steerable antenna solution for hand-off
A disp(disp2 4r)0.5 (2)
where r is the coverage radius and disp is the horizontal
shifting.
4.3 Hand off and Steerable Antenna
Steerable antennas can be used to cope with the movement of the
HAPs. HAPs movementshave been addressed in the past such as in
[1,15] and in [16] where various techniques havebeen proposed to
cope with various movements. Mechanism of antenna is proposed in
orderto counterbalance the horizontal displacement with the ideal
position of the HAPs and therelevant correction required being
specified using a Global Positioning System (GPS) [16].Steerable
antenna correction mechanism was proposed, which needs to be
applied on everyantenna individually [15]. However, this would
require a complex mechanical system with alarge number of motors
and therefore it would add significant weight to the payload.
It was preferable that HAPs system would employ some sort of
mechanically steerablemechanism but for a group of antennas
instead. As shown in Fig. 7, when HAPs movesupward, the antennas
will be pushed inward, and the center will move little upward. In
theother hand, when HAPs moves downward, the antennas will be
pushed outward, and theantenna at the center will move a little
downward.
In case of horizontal movement, the steerable antenna of the
centre cell is always pointingto the centre of the HAPs coverage
area, and all the antennas are interconnected with eachother.
Rotation adjustment can make all the antennas pointing to the
original position on theground, but elevation angle is different
from the original angle.
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Fig. 8 Model of artificial neuron
5 Neural Network Algorithm
Recently, ANN has been applied to many diverse problems. ANN is
one tool of artificialintelligence (AI). An ANN is a massively
parallel distributed processor that stores experi-mental knowledge;
this knowledge is acquired by a learning process and is stored in
the formof parameters of the ANN [17].
The ANN consists of a number of neurons arranged in a particular
fashion. The three basicelements of a neuron are the synaptic
weights (or weights), the summing junction, and theactivation
function. In Fig. 8 explains the fundamental component of the ANN,
an artificialneuron. Different activation functions include hard
limit, linear, log-sig. threshold k can beconsidered as one of the
weight. The ANN consists of more than one neuron. The output ofa
neuron k is given by:
uk =n
j=1WkjXj (3)
Yk = f (uk k) (4)where Xj ( j = 1, 2, . . . . . . . . . . . . .
. . , p) are the input, Wkj are weights, k is the threshold,F(..)
is the activate function, and Yk is the output of neuron.
ANN characteristics are massively parallel distributed
architecture, ability to learn andgeneralize, fault tolerance,
nonlinearity, and adaptively. The learning in ANN can be
unsu-pervised or supervised.
5.1 Radial Based Function Network
The RBFN consists of three different layers, an input layer, a
hidden layer, and an outputlayer as shown in Fig. 9. The input
layer acts as an entry point for the input vector; noprocessing
takes place in the input layer. The hidden layer consists of
several Gaussianfunctions that constitute arbitrary basis functions
(called radial basis functions); these basisfunctions expand the
input pattern onto the hidden layer space. This transformation from
theinput space to the hidden layer space is nonlinear due to
nonlinear radial-basis functions.
Two distinct phases of learning in the RBFN are selection of
enters of the radial basisfunctions and determination of linear
weights. Some of the methods for the selection ofRBFN centers are
random selection (based on the training patterns), unsupervised
selection,and supervised selection. Some of the methods for linear
weight determination are pseudo-
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Fig. 9 RBF neural network
inverse memory and LMS algorithm. These weight determination
methods and a mappingbetween the hidden unit space and the output
layer.
The output layer linearly combines the hidden layer responses to
produce an output pattern.The rationale behind the working of the
RBFN, a pattern-classification problem expressed ina
high-dimensional space is more likely to be linearly separable than
in a lower-dimensionalspace. The parameters of the RBFN weights (in
the output layer) and the positions and spreadsof the Gaussian
functions. A complete learning procedure can be found in [17].
Input nodes are RSS of MS and BS, traffic intensity of MS and
BS, steerable antenna,elevation angle of HAPs, delay, bandwidth,
HAPs position and distance between MS andnext BS. The output equals
the summation of hidden layer. The output decides whether thesystem
needs hand-off or not. When Y 1 and Y 2 are equal to 0 that mean no
hand-off will beperformance. If Y 1 and Y 2 are equal to 1, the
system will hand-off the mobile to chosen theBS.
Wk1 (n) = [Wk (n) , . . . . . . . . . . . . , Wk20(n)]
(5)Initialize all the following, the center value ji (0), the span
value j (0), weight vectorWK (0), expect W11 (0) = W21 (0) = 1.
Calculate the output of hidden layer and outputlayer are given
respectively by:
Zj = exp(((xi ij(n)))2/ 2
2 j
(n) (6)
Yk = R[ M
J=IWkj(n)ZJ
], k = 1, 2; M = 20 (7)
The error calculates by:
ek = dk yk (8)where dk {0, 1] desired pattern and update the
weight given by:
Wkj (n + 1) = Wkj (n) wekzj (9)
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where w and represent the learning rate of weight and center
respectively, update thecenter and span momentum:
ij (n + 1) = ij (n) + zjj
(xi ji (n)
) ekwkj (n) (10)
j (n + 1) = j (n) 2 zjj (n)
lnzj
ekwkj (n) (11)
where learning rate of span. Repeat the steps until the mean
square error convergence lessthan small number.
5.2 Hand-off Algorithm
After every small time interval, the simulator checked whether
the position of the HAPs hadchanged as shown in Fig. 10 and then
initiated hand-off if required.
If the position of the platform has changed, then all that users
have been affected. Then theusers must be added back into the
system and connected to the new cell. This is to eliminatethe case
where users are being dropped from a cell that is waiting for some
of its current usersto be connected to another cell. In this case
the cell will have a number of channels availableas soon as its
hand-off users release the channels they occupy. The point is to
ensure thatthese channels are available for the new hand-off users
coming to the cell. The affected usersare only a small proportion
of the total number of users within a cell.
Since the capacity is allocated on a case by case basis, the
overhead will not be significantlyhigh. There are major feature of
hand-off algorithm and several desirable feature of hand-off
algorithm should be fast, successful, the effect of hand-off on the
equality of servicesshould be minimum, should be maintain the
planning cellular borders to avoid congestion,the number of
hand-off should be minimized, target cell should be chosen
correctly minimaleffect on new cell blacking, procedure should be
minimize the number of connecting calldrop outs by providing
desired QoS.
Traffic intensity is the average number of calls simultaneously
in progress during a par-ticular period of time. It measured in
units of Erlangs. Thus 1 Erlang equals 1*3,600 callseconds. Traffic
intensity is equal to the summation of circuit holding time divided
by theduration of monitoring period.
I = Nct/T (12)
Where, I is traffic intensity, T is duration of monitoring
period is average holding time. Ncis total number of calls in
monitoring period.
There are two type of traffic which either infinite or finite.
Infinite traffic implies numberof call arrivals, each with a small
holding time. In other hand,when the number of sourcesoffering
traffic to group of trunks or circuits is comparatively small in
comparison to thenumber of circuits, this call finite traffic.
5.3 Comparison of Hand-off Approaches
The decision phase is the most important one in hand-off, the
network performance, satis-factions, efficiency, flexibility, and
complexity and reliability of the overall algorithm. Thedifferent
combinations of these criteria can be used to perform hand-off
decisions: Bandwidth(BW), Signal to Interference Ratio (SIR),
delay, response time, network coverage area, BiteError Rate (BER),
RSS, traffic load, and number of user (Tables 1, 2).
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Fig. 10 Hand off algorithm
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S. H. Alsamhi, N. S. Rajput
Table 1 Comparison of RSS andANN based network performance
Hand-off feature RSS ANN
Multi criteria No YesUser performance No MediumFlexibility Low
MediumComplexity Low HighEfficiency Low High
Table 2 Compaction of hand-off algorithm methods
Hand-offs features RSS RSS with threshold ANN
Resource management Signal strength Signal strength RSS, SIR,
velocity, availablepower, user performance,BW, etc.
Ping pong effect Yes Avoided AvoidedHand-off latency Low Low
ReducedNumber of hand-off High Reduced ReducedNumber of unnecessary
hand-off High Reduced LowNLOS Possible Possible Can be avoided
3 3.2 3.4 3.6 3.8 4 4.2 4.4 4.60.17
0.18
0.19
0.2
0.21
0.22
0.23
0.24
0.25
hand
off
rate
mean arrive time
RBF neural network
Backprop neural network
Fig. 11 Hand-off rate versus mean arrival time
6 Result
The RBFN is used for making a hand-off decision for chosen
neighbor BS. The input toneurons consist combination of parameters
which are required for taking hand-off decision.
Steerable antennas are used in HAPs, therefore movement of HAPs
(vertically or horizon-tally) had not any effect in hand-off
decision. Positioning MSs is obtained by apply the timingadvance
concept. When mean arrival time increases the hand-off rate
decreases smoothly asshown in Fig. 11. On the other hand, hand-off
rate increases when traffic intensity increasesas shown in Fig.
12.
The important of RBFN is shown in Figs. 11 and 12 for taking an
efficient hand-offs.Using number of parameters help RBFN to take
appropriate and efficient hand-off decisionand the unnecessary
hand-off reduces.
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0.65 0.66 0.67 0.68 0.69 0.7 0.71 0.72 0.73 0.74 0.750.2
0.21
0.22
0.23
0.24
0.25
0.26
0.27
0.28
0.29
hand
off
rate
traffic intensity
RBF neural networkBackprop neural network
Fig. 12 Hand-off rate versus traffic intensity
0 10 20 30 40 50 60 70 800.7195
0.72
0.7205
0.721
0.7215
0.722Handoff probability with diff speed
speed(Km/h)
Pro
balit
y
Fig. 13 Hand-off probability rate
Figure 13 clarifies the relationship between speed of user and
hand-off rate. So that, if thespeed increases the hand off
probability will increase.
7 Conclusion
A high performance hand-off algorithm provides many desirable
features by making appro-priate hand-off. The advantage of HAPs is
that it can provide services to the users either theyare getting
weak signals from the terrestrial systems or they are at the
covered area influ-enced by shadowing. The RSS, direction of MSs,
HAPs position, Traffic intensity, steerableantenna, elevation angle
of HAPs and delay are input of the neural networks. Effective
hand-off algorithm is done based on RBFN for combination of HAPs
and terrestrial systems. As aresult, hand-off rate and dropping
rate decrease as compared with other traditional methods.Therefore,
the hand-off rate increases when traffic intensity increases. As
well as hand-offrate decreases when mean arrival time
increases.
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in fourth generation heterogeneous networks.Communications
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foundation. (2nd ed.). USA: Tom Robbins.S. H. Alsamhi received the
B. E. from Department of Electronic Engi-neering (Communication
Division), IBB University, Yemen, in 2009.In 2009, He worked as
lecturer assistant in faculty of Engineering,IBB University. He
received M. Tech degree in Communication Sys-tems, Electronics
Engineering, Indian Institute of Technology (BanarasHindu
University), IIT (BHU), Varanasi, India in 2012. He is cur-rently
pursuing Ph.D. degree program in same department. His areaof
interest is in the field of wireless communication, Satellite
Com-munication, WiMAX, Communication via HAPS and Tethered
BalloonTechnology.
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An Intelligent Hand-off Algorithm
N. S. Rajput received Ph.D. degree in the area of Intelligent
DataAnalysis and Pattern Recognition in 2011, from Indian Institute
ofTechnology (BHU), Varanasi. He received the M. Eng. degree in
com-munication systems in 1997. He is presently working as an
AssistantProfessor (Stage-III) in the Department of Electronics
Engineering, IIT(BHU). His research interests include Intelligent
Techniques on Net-worked Communication and Computation.
123
An Intelligent Hand-off Algorithm to Enhance Quality of Service
in High Altitude Platforms Using Neural NetworkAbstract1
Introduction2 Classification of Hand-off3 Desirable Features of
Hand-off4 HAPs Movement4.1 Vertical Shifting4.2 Horizontal
Shifting4.3 Hand off and Steerable Antenna
5 Neural Network Algorithm5.1 Radial Based Function Network5.2
Hand-off Algorithm5.3 Comparison of Hand-off Approaches
6 Result7 ConclusionReferences