an intelligent hand-off algorithm to enhance quality

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

123

S. H. Alsamhi, N. S. Rajput

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.

123

An Intelligent Hand-off Algorithm

Fig. 1 Concept of HAP cellar

Fig. 2 Hand-off cellular system

123


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.

123


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:

123


Fig. 5 Vertical shifting up and down of HAPs

Fig. 6 Horizontal shifting of HAPs

123


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.

123


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-

123


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)

123


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).

123


Fig. 10 Hand off algorithm

123


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.

123


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.

123


References

1. Alsamhi, S. H., & Rajput, N. S. (2014). HAP antenna radiation pattern for providing coverage and servicecharacteristics. In Advances in computing, communications and informatics (ICACCI, 2014 InternationalConference on, 14341439).

2. kumar, A., & Purohit, H. (2013). A comparative study of different type of handoff strategies in cellu-lar system. International Journal of Advanced Research in Computer and Communication Engineering(IJARCCE), 2, 42784287.

3. Alsamhi, S. H., & Rajput, N. S. (2014). Performance and analysis of propagation models for efficienthandoff in high altitude platform system to sustain QoS. In Electrical, Electronics and computer science(SCEECS), 2014 IEEE Students Conference on, 16.

4. Stavroulakis, P. (2004). Fuzzy-neural applications in handoff. In P. Stavroulakis (Ed.), Neuro-fuzzy andfuzzy-neural applications in telecommunications (pp. 149234). Berlin Heidelberg: Springer.

5. Alsamhi, S. H. A., & Rajput, N. S. (2012). Methodology for coexistence of high altitude platform groundstations and radio relay stations with reduced interference. International Journal of Scientific & Engi-neering Research, 3, 17.

6. Alsamhi, S. H., & Rajput, N. S. (2015). An intelligent HAP for broadband wireless communications:Developments, QoS and applications. International Journal of Electronics and Electrical Engineering,3, 134144.

7. Alsamhi, S. H., & Rajput, N. S. (2014). Neural network in a joint HAPS and terrestrial fixed broadbandsystem. International Journal of Technological Exploration and Learning (IJTEL), 3, 344348.

8. Alsamhi, S. H. A., & Rajput, N. S. (2012). Interference environment between high altitude platform stationand fixed wireless access stations. International Journal of Engineering Research and Applications, 2,15081513.

9. Akoush, S., & Sameh, A. (2007). Mobile user movement prediction using bayesian learning for neural net-works. Proceedings of the International Conference on Wireless Communications and Mobile Computing,191196.

10. Kim, J.-S., Serpedin, E., Shin, D.-R., & Qaraq, K. (2008). Handoff triggering and network selectionalgorithms for load-balancing handoff in CDMAWLAN integrated networks. EURASIP Journal onWireless Communications and Networking, 2008, 114.

11. Saini, M., & Mann, S. (2010). Handoff schemes for vehicular ad-hoc networks: A survey. InternationalJournal of Innovations in Engineering and Technology, 8691.

12. Taaghol, P., Salkintzis, A. K., & Iyer, J. (2008). Seamless integration of mobile WiMAX in 3GPP networks.Communications Magazine, IEEE, 46, 7485.

13. Nasser, N., Hasswa, A., & Hassanein, H. (2006). Handoffs in fourth generation heterogeneous networks.Communications Magazine, IEEE, 44, 96103.

14. Park, J.-M., Ku, B.-J., Kim, Y.-S., & Ahn, D.-S (2002). Technology development for wireless communi-cations system using stratospheric platform in Korea. In Personal, Indoor and Mobile Radio Communi-cations, 2002. The 13th IEEE International Symposium. Vol. 4, pp. 15771581.

15. Capstick, M. H., & Grace, D. (2005). High altitude platform mm-wave aperture antenna steering solutions.Wireless Personal Communications, 32, 215236.

16. Axiotis, D. I., Theologou, M. E., & Sykas, E. D. (2004). The effect of platform instability on the systemlevel performance of HAPS UMTS. Communications Letters, IEEE, 8, 111113.

17. Haykin, S. (1999). Neural networks: A comprehensive 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.

123


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

an intelligent hand-off algorithm to enhance quality

Documents