Cross Layer Enhanced Adaptive Handoff Initiation Technique · horizontal handover is an Intra-technology based handoff that is, handoff between Node B’s of ... one MSC to another

Available online at www.worldscientificnews.com

( Received 17 January 2019; Accepted 05 February 2019; Date of Publication 06 February 2019 )

WSN 120(2) (2019) 154-167 EISSN 2392-2192

Cross Layer Enhanced Adaptive Handoff Initiation Technique

Jeremiah O. Abolade1,a, Augustus E. Ibhaze2,b, Agbotiname L. Imoize2,c,

Aderemi A. Atayero1,d

1Department of Electrical and Information Engineering, Covenant University, Ota, Nigeria

2Department of Electrical and Electronics Engineering, University of Lagos, Akoka-Lagos, Nigeria

a-dE-mail address: [email protected] , [email protected] , [email protected] , [email protected]

ABSTRACT

The population of mobile communication subscribers keeps increasing exponentially with a

growing industrial interest in the provision of improved quality of service. To maintain quality of service

in a heterogeneous network built for high capacity utilization, the communication network must be

stable as mobile users interact within the implemented coverage distribution. One of the basic

mechanisms behind this communication metric is its ability to communicate wirelessly with respect to

changing locations. Mobility is one critical benefit of mobile communication, which is made possible

through the process of Handoff. One of the key performances in mobile communication is Handoff

failure (call termination) rate, which is dependent critically on the handoff initiation mechanism. In this

paper, the effect of traditional single parameter of either signal-to-noise ratio (SNR) or throughput on

handover threshold and hysteresis was investigated. Consequently, a cross layer enhanced adaptive

handoff initiation algorithm using fuzzy logic was proposed. The proposed method exploits the

capability of soft computing to handle the unpredictable nature of the radio propagation environment.

Four critical handoff Network-link parameters were employed to vary the Handoff threshold and

Hysteresis in test the adaptive functionality of the algorithm. The proposed algorithm was seen to be

adaptive to throughput, SNR, mobile transmitting power (MTP) and mobile velocity, thereby resulting

in improved quality of service, marked with a decrease in handoff failure probability rate.

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Keywords: Base station, Fuzzy Inference, Handoff, Mobile Transmitting Power, signal-to-noise ratio,

Throughput, Velocity

1. INTRODUCTION

Mobility is one the characteristics of a cellular system. Even though the mobile phone

users are moving in and out of the cells, communication among users is still sustained. The

active call should be transferred from one cell to another in order to avoid call termination

during boundary crossing levels. This is accomplished by handoff process. As technology

integration evolves from single infrastructure technology to heterogeneous architecture of

evolving technologies, Oboyerulu et al. [1] establishes the need for efficient handoff algorithms

in terms of vertical and horizontal handoff techniques within the heterogeneous network. The

horizontal handover is an Intra-technology based handoff that is, handoff between Node B’s of

the same network technology as shown in Fig. 1, when the UE is transferred from one WLAN

access point to another. Vertical handover is an inter-technology based handover as shown in

Fig. 1, when WLAN transfer UE to LTE network.

Handoff is a mechanism of transferring ongoing calls from one cell to another or from

one MSC to another or one channel to another as users move through the coverage area of

diverse serving Node B’s [2]. Handover is the key process in wireless networks to determine

users QoS experience during mobility [3] [4]. Handoff is a growing research area in the field

of mobile WiMAX [5], HSPA+ and LTE [6]. According to Hamad [7], handover (HO) is a

concept, which aims to grant stability of connection while crossing diverse networks. With the

exponential growth rate in wireless mobile communication utilization, Beach [8] supposes that

the system requirements for higher data rate also increases exponentially due to the invention

of smart device technologies. The number of users and their demand for better resources

increases daily so today’s 3G is insufficient meeting these demands.

Figure 1. Horizontal and Vertical Handover Mechanism

Vertical Horizontal


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Conventional Handoff decision are generally single metric based which is not sufficient

for handoff initiation in the present sophisticated networks and environment (especially in an

urban area) [9]. The commonly used parameter in conventional handoff is Received Signal

Strength Indicator (RSSI), which measures the power present in a received radio signal [10].

Pollini [11] developed an algorithm that could initiate the handoff process based on the received

signal strength although limited by the ping-pong effect experienced in overlapping cell

coverage regions; resulting in unnecessary handoffs. Zhang and Holtzman [12] integrated a

margin between a couple of thresholds to eliminate the ping-pong effect while Marichamy et

al. [13] improved on the system by considering the application of hysteresis. In an attempt to

improve on the received signal strength-based handoff algorithm, Pahlavan et al. [14]

developed an algorithm based on artificial neural network to determine when to initiate handoff.

For better quality of service, an algorithm which is network-environment sensitive is needed.

Soft computing techniques deal with the uncertainty, partial truth, and approximation, and

depend on expert advice to reduce computational cost [15]. The aim of this work is to develop

a cross-layer sensitive handoff initiation algorithm to solve the real world problem of handoff.

This work only covers the handoff initiation phase as it serves as the basis for any handoff

optimization algorithm. The focus of this work is on how to effectively determine when handoff

should be requested rather than how it should be requested.

2. RELATED WORKS

Lal and Panwar [16], propose a dynamic hysteresis margin based handoff algorithm,

which uses distance between the mobile station, and base station and simulated in micro-cellular

environment. The authors used analytical method to test the algorithm performance using

handoff probability as the performance metric. Chang and Chen [2] proposed an adaptive

Hysteresis based horizontal handoff using distance between the BS and the MT to vary the

hysteresis value. The authors applied Okumura-Hata model, but several things apart from

distance can lead to degradation of signal such as co-channel interference, channel fading, etc.

Therefore, the algorithm can only be effective for a noiseless channel, which is an ideal

situation. Çeken et al. [17] proposed a fuzzy-based decision algorithm, which uses: user

preference, bandwidth, RSS, quality of service and network coverage. The algorithm, due to

the reduced Ping-Pong effect, reduced the number of unnecessary handoffs. Note that this

scheme only reduces the Ping-Pong effect and does not remove it. Mir and Bhatti [18] proposed

a Fuzzy-based handoff decision approach based on time triggering. Handoff design metrics

such as delay, throughput and number of handoffs were articulated among others. However,

handoff decision has to be performed by continuously sensing the arrival and departure patterns

of registered users. In Stemm and Katz [19] and Min-Hua et al. [20], more sophisticated

handover procedures, which considered more parameters apart from RSS, were investigated.

Chan et al. [21] established that fuzzy logic is advantageous in dealing with complexity of the

information in heterogeneous mobile access network.

Val et al. [22] proposed a spectrum handoff mechanism built with the capability of

interference detection and node switching within a predefined time threshold. The algorithm

was implemented using a Field Programmable Gate Array (FPGA) and OPNET simulator in

order to characterize the performance metric. The authors suggested more parameters to be

added in order to provide better quality of service. It is also noteworthy that the work did not


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indicate at elimination or minimization of Ping-Pong effect. In Aibinu et al. [23], the authors

used several input parameters to determine the handoff possibility in wireless mobile

communication by cascading Artificial Neural Network (ANN) and Fuzzy Logic for handover

decision. The fuzzy rule used in the work was adjudged able to make necessary handoff

decisions while avoiding the Ping-Pong effect when evaluated. Çalhan and Çeken [24]

proposed a fuzzy based vertical handoff decision controller by taking best network parameters

among available networks and compared those parameters with present network parameters as

set by vertical handoff decision controller for vertical handoff decision. In the vertical handoff

proposed by Jain and Tokekar [25], handover decision is dependent on coverage area and

velocity. The algorithm is applicable where speed is critical and it was simulated for particular

coverage range of network during which handoff is beneficial. The work reduces the

unnecessary handoff, which results in an improved QoS but only limited by the number of

factors considered; which are not enough in making handoff decision. For this reason,

researchers should consider more parameters for proper vertical handoff decision. In [7], the

author proposed a handover scheme between GSM and WiMAX mobile system in physical

layer mode. This solution provided a collection of high data rates, high mobility, and solving

the problem of traffic congestion in cellular mobile network using bit as the metric parameter.

3. TRADITIONAL ADAPTIVE BASED HANDOFF SIMULATION

3. 1. Signal-to-Noise Ratio (SNR) Based Handoff

The changing parameter is chosen to be Signal-to-Noise Ratio (SNR) while the speed,

throughput (best-case scenario), and Mobile Transmitting Power (MTP) are 0 km/h, 100%,

and 20 dBm, respectively as shown in Fig. 2.

Figure 2. Varying SNR while M.T.P., velocity and throughput are kept constant

0 10 20 30 40 50 60 70 80 900

10

20

30

40

50

60

70

80

90

100

No of Sample

SN

R/M

.T.P

./V

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city/T

hro

ugh

pu

t

SNR (dB)

M.T.P. (dBm)

Velocity (dBm)

Throughput (%)

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Figure 3. Handover Threshold vs SNR

Figure 4. Handover Hysteresis vs SNR

When the speed is maintained at 0 km/h, the throughput is kept at 100%, the mobile

Transmitting Power is also kept at 20 dBm and SNR changes, the handoff threshold vary

between -87.5 dBm to -107 dBm as shown in Fig. 3. In addition, the Hysteresis changes along

with respect to the SNR as shown in Fig. 4.

3. 2. Throughput Based Handoff

Fig. 5 shows the graph of SNR, MTP, and Velocity at 35 dB, 20 dBm, 0 km/h (best case

Scenario), respectively while the Throughput varies. It is worth noting that, the Handoff

10 15 20 25 30 35-110

-105

-100

-95

-90

-85

SNR (dB)

Ha

nd

ov

er T

hre

sho

ld (

dB

)

Graph of Handover Threshold Against SNR

10 15 20 25 30 356.5

7

7.5

8

8.5

9

9.5

Graph of Handover Hysteresis Against SNR

SNR (dB)

Han

do

ver

Hy

ster

esis

(d

B)


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threshold and hysteresis continuously vary with respect to the variation in the throughput as

shown in Fig. 6 and Fig. 7, respectively. It is also seen that a sharp fall exist in the handoff

threshold of Throughput-based handoff when compared with SNR-based handoff which shows

the critical nature of the Throughput in the algorithm.

Figure 5. Varying Throughput while M.T.P., Velocity and SNR are kept constant

Figure 6. Handoff Threshold vs Throughput


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Figure 7. Handoff Hysteresis vs Throughput

3. 3. Methodology

In this paper, four parameters were used as opposed to single parameter-based algorithms

used in traditional adaptive handoff systems. The parameters are Signal to noise ratio (SNR),

Mobile Transmission Power (MTP), Speed, and Throughput. These parameters are the inputs

to the Fuzzy Inference System to produce Handoff Hysteresis and Threshold as the output of

the fuzzy logic shown in Fig. 8. The implementation of this algorithm of four input parameters

in deciding the two metrics for handoff decision is as shown in Fig. 9.

Figure 8. Block diagram of the Methodology


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Figure 9. FIS for threshold and Hysteresis

Each of the input variables is divided into three fuzzy sets each defined with three

linguistic variables, which are LOW, NEUTRAL, and HIGH. Seven linguistic variables were

used for the outputs, which are LOWEST, LOWER, LOW, NEUTRAL, HIGH, HIGHER, and

HIGHEST. The membership functions used are Gauss and Triangular Membership Functions.

These membership functions were used because they are suitable for the complex situation at

hand as well as for efficient computation.

The philosophy behind the fuzzy rules:

i. If the Throughput is “LOW”, the SNR is “LOW”, the MTP is “HIGH”, and the Speed

is “HIGH”. For this, the handoff should be encouraged as fast as possible. Therefore,

the Threshold should be increased to “HIGHEST” and the Hysteresis should be

“LOWEST”.

ii. If the Throughput is “HIGH”, the SNR is “HIGH”, the MTP is “LOW”, and the Speed

is “LOW”. For this, the handoff should be discouraged. Therefore, the threshold should

be decreased to “LOWEST” and the Hysteresis should be increased to “HIGHEST”.

Other rules philosophy states that if there is more support for encouraging a handoff, the

threshold is improved and the hysteresis is decreased. Otherwise, there is more support for

handover discouragement by the input parameter, there would be decrease in threshold and

increase in hysteresis. The degree of how FIS output change for encouragement or

discouragement is dependent on the number of input variables that support the decision. The

higher the rules the better performance, which reduces the quantity of unnecessary handover

and delay, involved in handover [3].

The membership functions (MF) used in the proposed algorithm for inputs and output are

Gauss and Triangular membership functions, respectively because they are more suitable to

model the possibility distributions derived from the natural phenomena of the parameters under

consideration. The mathematical model of Gauss MF and Triangular MF are given in Eqn. (1)

and (2), respectively, and Table 1 represents the simulation parameters:


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𝑓(𝑥; 𝜎, 𝑐) = 𝑒−(𝑥−𝑐)2

2𝜎2 (1)

𝑇𝑟𝑖𝑎𝑛𝑔𝑙𝑒 (𝑥; 𝛼, 𝛽, 𝛾) = max (min (𝑥−𝛼

𝛽−𝛼,

𝛾−𝑥

𝛾−𝛽) , 0) (2)

where:

𝜎 = 𝑤𝑖𝑑𝑡ℎ of the Gaussian function, 𝑐 = 𝑐𝑒𝑛𝑡𝑒𝑟 𝑜𝑓 𝑡ℎ𝑒 𝑔𝑢𝑎𝑠𝑠𝑖𝑎𝑛 𝑐𝑢𝑟𝑣𝑒)

α, β, γ represent starting point, peak point and final point respectively which are x coordinates

where (α<β< γ).

Table 1. Simulation parameters

Parameter Configuration

FIS type mamdani

AND method min

OR method max

Implication min

Aggregation max

Defuzzification centroid

SNR range [10 35] dB

MTP [20 32] dBm

Velocity [0 130] Km/Hr

Throughput [0 100] %

Threshold [-110 -40]

Hysteresis [0 10]

4. RESULTS AND DISCUSSION

The proposed algorithm focuses on the removal of the Ping-Pong problem, corner effect

and to avoid the risk of unnecessary handoffs by optimizing the values of the threshold and

hysteresis based on the value of the input parameters. It is worthy of note that once the threshold

value for handoff is met, the effect of a mobile device spontaneously roaming between

overlapping coverage zones will be eliminated. This is largely because it will stay connected in

either a coverage zone or handoff to another coverage zone absolutely, thereby eliminating the

ping-pong effect. Further to this, the allowable signal strength difference between the serving


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Node B and the monitoring Node B needed to initiate handoff, that is; the hysteresis value must

also be satisfied to absolutely mitigate this effect. The same goes for other handoff limiting

parameters. The results show inputs-Outputs variation as follows; when the SNR is 22.5 dBm,

MTP is 26 dBm, Velocity is 65 km/h and the throughput is 50%, the resultant handoff Threshold

and Hysteresis is 74.1 dBm and 4.94 dBm.

The Input-Output relation is modeled as presented in Eqn. (3):

𝑦𝑗𝑖 = ∑ ∏ ℎ𝑚𝑥𝑖𝑛𝑚=1

𝑏𝑖=1 (3)

where:

𝑏 = 𝑘𝑛 is the number of fuzzy rule generated

ℎ𝑚 is the mth row of FIS matrices of the rules in the FIS

𝑘 is the number of fuzzy membership function

(In this case, k = 3 that is; Low, Normal and High)

𝑛 is the number of inputs (in this case, n = 4)

𝑗 is the number of outputs for 1 ≤ 𝑗 ≤ 2

𝑥𝑖 is ith column of the input matrix for 1 ≤ 𝑖 ≤ 𝑏

𝑦𝑗𝑖 is the possible output response of the FIS

ℎ = [

ℎ11 ⋯ ℎ1,𝑛

⋮ ⋱ ⋮ℎ𝑏1, ⋯ ℎ𝑏𝑛

] (4)

𝑥 = [𝜃11 ⋯ 𝜃1𝑏

⋮ ⋱ ⋮𝜃𝑛1 ⋯ 𝜃𝑛𝑏

] (5)

𝑦 = [

𝑦𝑗1

:𝑦𝑗𝑏

] (6)

where:

ℎ is the matrix of the FIS rules and the matrix element represent the strength of each rule as

stated in the MF plane, x is the matrix of the input combination, and y is the output matrix

combination. It should be noted that x is a Gaussian function and y is a triangular function.

Therefore, Eqn. (3) is treated fuzzifully.

Furthermore, evaluation of the Algorithm was carried out using “eval” function by

randomly selecting the input sequence. The variation of the input parameters is as shown in Fig.

10. The resultant outputs (that is, Handoff threshold and Hysteresis) are as shown in Fig. 11.

The outcome can be seen to be highly adaptive in that variation in the input parameters causes

a corresponding change in the Handoff Threshold and Hysteresis as compared to traditional

handoff where both the Handoff threshold and hysteresis remain unchanged irrespective of the

variation in the network parameters. The combination of the four parameters in making handoff

initiation decision improves the Quality of Service as noise, speed, and throughput are put into

consideration. The use of Mobile Transmission Power also enhanced the power consumption


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by mobile users which in turn optimized the overall power consumption via mobile

communication.

Figure 10. Input sequences for Performance Evaluation

Figure 11. Resultant Adaptive Handoff Threshold and Hysteresis

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 200

20

40

60

80

100

120

140

No of Sample

SN

R/M

.T.P

./V

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hro

ugh

pu

t

SNR (dB)

M.T.P. (dBm)

Velocity (Km/h)

Throughput (%)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20-120

-110

-100

-90

-80

-70

-60

-50

-40

-30

-20

-10

0

10

No of Sample

Ha

nd

ov

er T

hresh

old

/Hy

ste

resis

Handover Threshold (dBm)

Handover Hysteresis (dBm)


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5. CONCLUSION

Though several works have been reported in this research direction, the application of

soft computing in this area provides a better way of handling problems associated with mobility

within a wireless mobile network. The proposed methodology provides an effective way of

handling handoff issues like Ping-Pong, handoff failure rate, and network latency as it allows

only the necessary handoff without Ping-Pong with a focus on the quality of service. More so,

the parameters under consideration provide a holistic quality of service as the link quality,

channel throughput, UE velocity is been monitored. Future work would focus on the

implementation of this algorithm in the existing wireless communication systems.

Acknowledgements

Special thanks to Covenant University and University of Lagos for the sponsorship of this research work.

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Cross Layer Enhanced Adaptive Handoff Initiation Technique · horizontal handover is an Intra-technology based handoff that is, handoff between Node B’s of ... one MSC to another