An Improved Location-Based Handover Algorithm

for GSM Systems

Rong-Terng Juang, Hsin-Piao Lin, and Ding-Bing Lin

Institute of Computer and Communication, National Taipei University of Technology, Taipei, Taiwan

s1669013@ntut.edu.tw, hplin@en.ntut.edu.tw, dblin@en.ntut.edu.tw

Abstract—The variation of signal strength caused by shadowings is a random process, and handover decision mechanisms based on measurements of signal strength induce the “ping-pong effect”. This paper proposes an improved handover algorithm, which identifies the correlation among shadowing components based on the estimates of mobile velocity, to suppress the pingpong effect. The impacts of the estimation errors of velocity on handover performance are investigated. The simulation results indicate that the number of un-necessary handover can be reduced 9~17 percent by the proposed approach, compared to the conventional method, while the signal outage probability remains similar. Keywords-handover; mobile location; mobile velocity estimation; shadow fading. I. INTRODUCTION

Handover refers to the mechanism by which an ongoing call is transferred from one base station (BS) to another. The performance of the handover mechanism is extremely important. Frequent handovers reduce the quality of service (QoS), increase the signaling overhead on the network, and degrade throughput in data communications. Many metrics have been used to support handover decisions, including received signal strength (RSS), signal to interference ratio (SIR), distance between the mobile station and BS, traffic load, and mobile velocity. The conventional handover decision compares the RSS from the serving BS with that from one of the target BSs, using a handover margin, a constant handover threshold value. The selection of the margin is crucial to handover performance. If the margin is too small, numerous unnecessary handovers may be processed. Conversely, the QoS could be low and calls could be dropped if the margin is too large. The fluctuations of signal strength associated with shadowing cause a call sometimes to be repeatedly handed over back and forth between neighboring BSs, in what is called the

“ping-pong effect”. Over recent years, many investigations have addressed handover algorithms for

cellular communication systems. A local averaging technique, which moves fast fading component from the received signal strength, was proposed in [1] to allow the conventional handover decision reacting more quickly to corner effects. A timer-based hard handover algorithm was presented in [2] to prevent unnecessary handovers caused by fluctuations due to shadowing, by which the choice of timer interval introduces the tradeoff between handover number and handover delay. A dynamic handover margin decision based on a traffic balancing rule was proposed in [3] to resize the cells according to the spatial variability of traffic. A speed-sensitive handover algorithm in a hierarchical cellular system was described in [4], in which micro-cells serve the slowly-moving mobiles, and macro-cells serve fast-moving mobiles. In [5] and [6], RSS, mobile location and velocity were used as metrics for making handover decisions using fuzzy logic. A table lookup approach, proposed in [7], determines handover margins based on the mobile location, the mean signal intensity and the standard deviation thereof. Distance hysteresis for mitigating the effect of shadowings on handover performance was presented in [8]. Making handover decisions in various scenarios was presented in [9], in which a suitable handover decision mechanism is selected when the mobile station is located in an area with a pre-defined handover scenario.

In the literature, however, most handover algorithms, which are based on information about mobile location, suffer from a lack of practicability. The computational complexity of making a handover decision using fuzzy logic is excessive, and establishing and updating a lookup table to support a handover margin decision is time-consuming. The selection of a handover algorithm based on the handover scenario only succeeds in cases that the propagation environment is similar to one of the pre-classified environments, and involves complicated processes to define the handover scenarios. It also relies on an updated database when applied in a new mobile user environment. Furthermore, most studies assume that mobile location can be perfectly determined using GPS (Global Positioning System), which is not available for most existing mobile telephones. In reality, the performances of available location estimators are far from that obtainable using GPS technique.

This paper proposes an improved handover algorithm based on the estimates of mobile location (not using GPS) and velocity in a lognormal fading environment. The proposed algorithm outperforms the conventional method in making handover decisions for cellular systems by using location and velocity to identify the correlation among shadowing effects. Moreover, the computational complexity of the proposed algorithm is low, and the algorithm does not employ a database or lookup table.

II. PROPOSED HANDOVER ALGORITHM A. System Model

In a GSM system, when a mobile station moves from BS1 to BS2, the signal strength is measured and reported to the network in a constant 480 ms time interval [10] to support a handover decision. The signal power level in decibels is the sum of two propagation terms, namely path loss and shadowing; fast fading is ignored because it is averaged out. Accordingly, the signal levels received from BS1 and BS2 at discrete time instants tk= kτ (where τ represents the time interval of 480 ms within which the signal strength is measured), are given by P1[k] and P2[k], respectively,

1 1 1[ ] [ ] [ ] P k m k u k (1)

2 2 2[ ] [ ] [ ]P k m k u k (2) where 1 m and 2 m are the received signal powers from BS1 and BS2, respectively, in terms only of path loss, and 1u and 2u are the respective shadowings. The auto-correlation coefficient, ii , of the shadowings is commonly assumed to be an exponential function [11],[12],

1 22

{ [ ] [ ]} exp( ), 1,2,i iii

i

E u k u k d d i

(3)

where i is the standard deviation of shadowings; 2 1d V k k ( V is mobile

velocity, non-negative number), and d is the decay distance (or correlation distance), which ranges from around 25 to 100 m over urban, light urban, and suburban terrain

[13]. The cross-correlation coefficient, ij , of shadowings is called the “site-to-site

correlation” [14] and is calculated as

{ [ ] [ ]}, 1, 2, i ji j

iji j

E u k u ki

(4)

The correlation depends on 1) the angle between the two paths along the mobile station to BS1 and BS2, and 2) the relative values of the two path lengths. Jay Weitzen et al. verified that the shadowing components are slightly correlated even at small angles [13]. B. Proposed Handover Algorithm

Define 21[ ]P k as the difference between signal powers received from BS2 and BS1 at time index k:

21 2 1 2 1 2 1 21 21[ ] [ ] [ ] { [ ] [ ]} { [ ] [ ]} { [ ] [ ]}P k P k P k m k m k u k u k m k u k (5) where 21m represents the difference between signal powers received from BS2 and BS1 in terms of path loss only, 21u represents the difference between the shadowings

along the two paths. A handover from BS1 to BS2 occurs at time index k if the following two criteria are satisfied simultaneously. Criterion 1: P21 [k] ≥ h Criterion 2: P 21 [k] − P21 [k −ξ] ≥ h where h is the handover margin, and ξ is a positive non-zero integer, which needs to be carefully decided. In fact, criterion 1 is applied in making conventional handover decisions. Because of shadowing, unnecessary handovers may be performed if a handover decision is based only on this criterion. Therefore, criterion 2 is imposed to improve the handover performance by determining whether path loss dominates the variation in the received signal strength.

Assume 21[ ]u k and 21[ ]u k are highly correlated, such that the correlation coefficient approaches unity; then, the difference between 21[ ]P k and 21[ ]P k can be approximated as

( ) ( )21 21 2 2 1 1 2 1[ ] [ ] { [ ] [ ]} { [ ] [ ]} up downP k P k m k m k m k m k m m (6)

where ( )2upm and ( )

1downm are the increase and degradation of the signal powers

received from BS2 and BS1 in terms of path loss, due to motion of the mobile station. Consequently, the difference between signal powers is always chiefly a function of path loss but not of shadowings. Restate, the proposed algorithm ensures that the signal power received from the target BS is h dB higher than that received from the serving BS (criterion 1), and that the difference between the signal powers is dominated by path losses associated with motion of the mobile station(criterion 2). Hence, unnecessary handovers caused by fluctuations in shadowings can be avoided.

In the proposed algorithm, ξ is critical to handover performance. The decided ξ must guarantee highly correlation between 2[ ]u k and 21[ ]u k , and sufficient space for signal variation caused by path loss. If ξ is too large, criterion 2 is always met and is helpless for the handover decisions. Conversely, the signal dose not vary if ξ is too small. How to decide a suitable value of ξ is explained below.

Given 1[ ]u k , if the standard deviations of shadowings are assumed to be equal, such that 1 2 u , then 2[ ]u k , 1[ ]u k and 2[ ]u k can be expressed as follow, based on the Gauss-Markov process.

22 12 1 12 1[ ] [ ] 1u k u k X (7)

22 11 1 11 2[ ] [ ] 1u k u k X (8)

22 22 2 22 3[ ] [ ] 1 u k u k X (9)

where 1X , 2X and 3X are identical independent Gaussian processes with

zero-mean and variance 2u , and 1 1 1 2 1 3{ [ ] } { [ ] } { [ ] } 0E u k X E u k X E u k X .

Assume (1) 12 21 c and (2) 11 22 a , the following can be proven.

21 2 1 2{ [ ] [ ]} { [ ] [ ]} c uE u k u k E u k u k (10)

21 1 2 2{ [ ] [ ]} { [ ] [ ]} a uE u k u k E u k u k (11)

21 2 2 2{ [ ] [ ]} { [ ] [ ]} c a uE u k u k E u k u k (12)

The correlation between 21[ ]u k and 21[ ]u k is

21 21 2 1 2 12 2

{ [ ] [ ]} {[ [ ] [ ]][ [ ] [ ]]}

(1 )(2 ) exp( / )(1 )(2 )a c u c u

E u k u k E u k u k u k u k

V d

(13)

The correlation coefficient must exceed a threshold, T ; that is,

exp( / )(1 )c TV d , such that

ln( )1

T

c

dV

(14)

Figure 1 displays the flowchart of the proposed handover decision. The mobile station provides measurement reports to network to support handover decisions within constant time intervals, τ . This data is buffered in the memory for the mobile location estimation proposed in [15]. The handover alarm is triggered when the signal power received from the serving BS is below a threshold, then, the availability of the target BS is verified according to criterion 1. If the target BS meets criterion 1, the data buffered in the memory is fetched to estimate the location and velocity of the mobile station, saving the overhead cost of calculating location since the mobile station is not continuously tracked. The value of ξ can be obtained from mobile velocity using (14) to confirm criterion 2. Consequently, a handover occurs if the target BS satisfies criteria 1 and 2 simultaneously, otherwise the serving BS remains unchanged and the handover decision is made again at the next time.

The results of the simulations using the proposed handover algorithm are compared with those obtained using the conventional method. A software package, SignalPro by EDX Engineering, was used to help the simulation. SignalPro includes a set of planning tools for wireless communication systems. Figure 2 shows the simulation environment that covers an area of 1.6 x 1.4 Km2. The trajectory from “A” to “B” represents a route through which the mobile station moves. Polygons are buildings with different heights. Seven BSs with omni-directional antennae are

designated by encircled crosses ( ). The height of each BS is 35m and the mean and standard deviation of their transmitting power (EIRP) are 42.6dBm and 3.5dB, respectively. Walfisch-Ikegami model, which had been verified to predict accurately propagation path loss in urban areas with small cells [16], was applied to simulate the path loss. Shadowings were simulated according to the model proposed in [17], where

d = 65 m and c = 0.1. The mobile station moved along the trajectory in Fig. 2 at

a constant speed of 30Km/h. The sampling interval for reporting measurements is 0.48s. The handover alarm threshold, handover margin, and correlation threshold were set to –80dBm, 6dB and T = 0.85, respectively. Figure 3 is a typical comparison between the received signal time series obtained by the conventional method and that obtained by the proposed handover algorithm when the mobile station moves along the beginning of the trajectory in Fig. 2. In the simulations, the mobile velocity was assumed to be perfectly estimated and the standard deviation of shadowing was set to 9dB. The results show that the conventional method involves more handovers whereas the proposed algorithm prevents unnecessary handovers.

Figure 1. Flowchart of proposed handover decision.

Figure 2. The simulation environment.

Figure 3. Comparison of signals received according to the conventional method (top

plot) and the proposed handover algorithm (bottom plot).

III. ANALYSIS OF HANDOVER PERFORMANCE WITH LOCATION ERRORS

The proposed algorithm requires the mobile velocity to determine ξˆ . Since the GPS receiver is not available in most existing mobile devices, considerations must be given to the effects of the estimation errors of velocity upon handover performance. The velocity of the mobile station was estimated based on Doppler frequency shift in [18]. However, the estimated Doppler frequency is unreachable in most standards of mobile cellular systems. This paper presents a means of estimating mobile velocity based on mobile location estimations.

For simplicity, the problem is reduced to the one-dimensional case. The mobile location estimate at time index k is modeled as

ˆ[ ] [ ] LL k L k n (15)

where [ ]L k is the actual mobile location, and Ln represents the location error,

which is modeled as a zero-mean Gaussian process with variance 2L , as in [19].

Previous location information is used to estimate the current velocity. The size of the

estimation window is M, so the estimated locations ˆ ˆ ˆ{ [ ], [ 1],..., [ 1]L k L k L k M

are used to estimate mobile velocity. An adequate integer (1 2 and ( ) is even)m m M M m is chosen such that the mean of

ˆ ˆ{ [ ],..., [ ( ) / 2 1]L k L k M m can be used as a more accurate version of

ˆ[ ( ) / 4 0.5]L k M m , which is denoted by '̂[ ]L i , and the mean of

ˆ ˆ{ [ ( ) / 2],..., [ 1]}L k M m L k M can be used as a more accurate version of

ˆ[ (3 ) / 4 0.5]L k M m , which is denoted by '̂[ ]L j . Then, the estimated mobile

velocity at time index k has the form

' 'ˆ ˆ[ ] [ ]ˆ [̂ ] [ ]

1 v

L i L jV v k v k n

m

(16)

where vn is the error in the estimated velocity and is also a zero-mean Gaussian

process with variance 2 2 2 2 2[4 /( )] ( )v LM m M m . Given suitably chosen M and

m , the mobile velocity can be estimated accurately. As a simple example, M = 7 and

m = 3 are chosen, as presented in Fig. 4, where '̂[3]L and '̂[5]L are

'̂ ˆ ˆ ˆ ˆ ˆ[3] { [1] [2] [3] [4] [5]}/ 5L L L L L L (17)

'̂ ˆ ˆ ˆ ˆ ˆ[5] { [3] [4] [5] [6] [7]}/ 5L L L L L L (18)

The estimated mobile velocity is

' 'ˆ ˆ ˆ[̂7] [5] [3] / 2 [7] vV v L L v n (19)

where the variance of vn is 2(1/ 25) L .

However, the mobile location is a two-dimensional problem in reality. The estimates of location on the horizontalaxis and the vertical-axis at time index k are respectively expressed as

2 2ˆ ˆ ˆ( [ ]) ( [ ])x yV v k v k (21)

where ˆ [ ]xv k and ˆ [ ]yv k are the velocity estimations on the horizontal-axis and the

vertical-axis, respectively. Denote the actual velocity of the mobile as V and assume

the variances of the error terms, in ˆ [ ]xv k and ˆ [ ]yv k , equal 2v , the probability

density function (p.d.f.) of V̂ is a Rice distribution with Rice factor 2 2/(2 )vK V

[14],[20],

2

ˆ 02 2

ˆ ˆ ˆ 2ˆ( ) exp{ }exp[ ] ( )2v

v v v

V V V Kf V K I

(22)

where 0 ( )I z is a modified Bessel function of the first kind and zeroth-order. A larger

K, which corresponds to a faster mobile or a lower v yields a more accurate

estimate of velocity because the p.d.f. curve is sharper. Moreever, redefine (14) as

ˆ ˆln[ /(1 )] /( ),T c d V distributes as

ˆ2ˆ( ) ( )ˆ ˆVf f

(23)

where ln[ /(1 )] /T c d . Figure 5 plots the p.d.f. curves of ̂ given

various location errors ( L ). Given the parameter settings

{ 0.1, 0.85, 65 , 0.48 , mobile velocity = 30 Km/h}c T d m s , the actual

0.929. The accuracy of estimate is very high because (1) ̂ is run off during

handover decisions, and (2) ̂ is a positive non-zero integer, which resulting in ̂ =1

with very high probability. Handover number and handover delay (referred also as crossover point) are often

used as performance measures [1], [2], [17]. In our study, however, signal outage probability, defined as the rate that the received signal from serving BS is less than a threshold, is substituted for handover delay because the cell shape of each BS is an irregular polygon instead of a regular hexagonal structure, which resulting in difficulty in defining handover delay. With reference to the same case as simulated in Section II, Fig. 6 compares the performances of the proposed algorithm and the conventional method averaged over 2000 iterations. The standard deviations of shadowings were set to 3, 6 and 9 dB, and the location errors were set to 0, 30, 60 and 90 m. In estimating the velocity, M and m were chosen as 9 and 3. The horizontal and vertical axes in Fig. 6 respectively represent the total number of handovers and the signal outage probability associated with the outage threshold at –95 dBm. Symbols

“○”, “ ＋ ” and “□” indicate the performance in 3, 6 and 9 dB shadowing

environments, respectively. The solid line and the dotted line represent the results obtained using the conventional method and the proposed algorithm, respectively. Using the proposed algorithm reduces the number of handovers and only slightly increases in signal outage probability. The performance improvement measured as the decrease in the number of handovers over that obtained using the conventional method ranges from 9 to 17 percent.

Figure 4. An example of estimation of mobile velocity from location. (M=7, m=3)

Figure 5. Probability density functions of ξ ˆ associate with different location errors.

Figure 6. Comparison of handover performances by simulations.

IV. CONCLUSIONS An improved location-based handover algorithm has been presented. The

algorithm suppresses the ping-pong effect in cellular systems base on the estimate the mobile velocity. The effects of location errors on handover performance were examined since the GPS-location is not available in most existing mobiles. The proposed method exploits the correlation properties of shadowings to avoid unnecessary handovers in the overall environment. The simulations indicate that the number of un-necessary handovers can be reduced 9~17 percent by the proposed method compared to the conventional one, while the signal outage probability remains similar. Besides, the computational complexity of the proposed algorithm is low and no database or lookup table is required.

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