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Validation of an improved Validation of an improved location-based handover location-based handover algorithm using GSM algorithm using GSM measurement datameasurement dataHsin-Piao Lin; Rong-Terng Juang; Ding-Bing Lin

IEEE Transactions on Mobile Computing, Vol. 4, No 5, Sep. 2005

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Outline Outline

• Introduction• Proposed Handover Algorithm• Analysis of Handover Performance with

Location Errors• Verifying Performance Using GSM

Measurement Data• Conclusion

IntroductionIntroduction

• HANDOVER – the mechanism by which an ongoing call is transferred

from one base station (BS) to another.

• Frequent handovers influence the QoS, increase the signaling overhead on the network, and degrade throughput in data communications.

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IntroductionIntroduction (cont’d) (cont’d)

• Metrics used to support handover decision– received signal strength (RSS),– signal to interference ratio (SIR), – distance between the mobile and BS,– traffic load, and – mobile velocity

• RSS mostly commonly used– constant handover threshold value (handover margin)

• too small unnecessary handovers• Too large the QoS could be low and calls could be dropped

– ping-pong effect• Caused by the fluctuations of signal strength associated with shadow fadings

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IntroductionIntroduction (cont’d) (cont’d)

• Most handover algorithms that 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,

• Establishing and updating a lookup table to support a handover margin decision is time-consuming

• Selecting a handover algorithm based on the handover scenario – only succeeds in the preclassified environments, and – involves complicated processes to define the handover

scenarios. – relies on an updated database• Assuming GPS capable mobile telephone

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IntroductionIntroduction (cont’d) (cont’d)

• The proposed handover algorithm– based on the estimates of mobile location (not using GPS)

and velocity in a lognormal fading environment.• identify the correlation among shadowing effects

– was applied to a living GSM system in urban Taipei city. – Low computational complexity– does not employ a database or lookup table

• signal level = path loss + shadow fading– The variation in the signal caused by shadow fading

depends on the location and velocity of the mobile station

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System ModelSystem Model

• the signal power levels received from BSx at time index k: Px[k] = mx[k]+ux[k]

– mx is the received signal powers from BSx in terms only of path loss,

– ux is the respective shadow fadings

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System ModelSystem Model (cont’d) (cont’d)

• The autocorrelation coefficient of the shadow fadings is commonly assumed to be an exponential function [11][12]

– σi is the standard deviation of shadow fadings;

– △d = V ． | k2 - k1 | ． τ

• V is mobile velocity, τ = 480 ms

– is the decay distance (or correlation distance)

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d

[11] M. Gudmundson, “Correlation Model for Shadow Fading in Mobile Radio Systems,” Electronics Letters, vol. 27, no. 23, pp. 2145-2146, Nov. 1991.[12] D. Giancristofaro, “Correlation Model for Shadow Fading in Mobile Radio Channels,” Electronics Letters, vol. 32, pp. 958-959, May. 1996.

System ModelSystem Model (cont’d) (cont’d)

• The cross-correlation coefficient of shadow fadings

– The correlation depends on • the angle between the two paths along the mobile to BS1 and BS2,

and• the relative values of the two path lengths.

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Proposed Handover AlgorithmProposed Handover Algorithm

• The difference between signal powers received from BS2 and BS1 at time index k:

• A handover from BS1 to BS2 occurs at time index k if

– Because of shadowing, unnecessary handovers may be performed if a handover decision is based only on Criterion 1.

– Criterion 2 is imposed to improve the handover performance by determining whether path loss dominates the variation in the received signal strength.

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Proposed Handover AlgorithmProposed Handover Algorithm (cont’d)(cont’d)

• Assume u21[k] and u21[k-ξ] are highly correlated, such that the correlation coefficient approaches unity

• The difference between P21[k] and P21[k-ξ]

• the difference between signal powers is always chiefly a function of path loss but not of shadow fadings

• 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 shadow fadings can be avoided.

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Proposed Handover AlgorithmProposed Handover Algorithm (cont’d)(cont’d)

• ξ is critical to handover performance– guarantee high correlation between u21[k] and u21[k-ξ], and

sufficient space for signal variation caused by path loss– too large Criterion 2 is always met– too small the signal dose not vary

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Proposed Handover AlgorithmProposed Handover Algorithm (cont’d)(cont’d)

• the standard deviations of shadow fadings are assumed to be equal, such that σ1 = σ2 = σu

• Given u1[k], then based on the Gauss-Markov process

– where X1, X2, and X3 are identical independent Gaussian processes with zero-mean and variance σu

2 and

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Proposed Handover AlgorithmProposed Handover Algorithm (cont’d)(cont’d)

• Assume ρ12 = ρ21 = ρc and ρ11 = ρ22 = ρa, then

• The correlation between u21[k] and u21[k-ξ] is

• The correlation coefficient between u21[k] and u21[k-ξ] must exceed a threshold ρT, then

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location estimation using [15]

[15] D.B. Lin, R.T. Juang, H.P. Lin, and C.Y. Ke, “Mobile Location Estimation Based on Differences of Signal Attenuations for GSM Systems,” Proc. IEEE Soc. Int’l Conf. Antennas and Propagation, vol.1, pp. 77-80, June 2003.

• Simulation using SignalPro by EDX Engineering– includes a set of planning tools for wireless communication

system

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1.4

KM

1.6 KM

omnidirectional antenna

The height of each BS is 35 mthe mean and standard deviation of their transmitting power (EIRP) are 42.6 dBm and 3.5 dB.

The Walfisch-Ikegami model was applied to simulate the path loss.

= 65 m, ρc =0.1V = 30 km/h, ρT =0.85

handover alarm threshold = -80 dBmhandover margin = 6dB

d

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Analysis of Handover Analysis of Handover Performance with Location Performance with Location ErrorsErrors• 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.

18[18] G. Azemi, B. Senabji, and B. Boashash, “A Novel Estimator for the Velocity of a Mobile Station in a Micro-Cellular System,” Proc. Int’l Symp. Circuits and Systems, vol. 2, pp. 212-215, May 2003.

• For one-dimensional case, the estimated location

– L[k]: the actual mobile location

– nL: the location error, which is modeled as a zero-mean Gaussian process with variance σL

• For two-dimensional case

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• The accuracy of the estimate of ξ is very high because – It is run off during handover decision– It is a positive nonzero integer, which resulting in ξ=1 with very

high probability

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• Using the proposed algorithm reduces the number of handovers (9-17%) and only slightly increases in signal outage probability.

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Verifying Performance using Verifying Performance using GSM Measurement DataGSM Measurement Data• The proposed handover algorithm was applied to a living GSM system

(1,800MHz) in urban Taipei city.

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1.6

KM

2.1 KM

averaged cell radius of around 330 m.

The mean and standard deviation of building heights are 20.3 m and 14.4 m.

The average and standard deviation of BS heights are 26.4 m and 10.2 m.

Verifying Performance using Verifying Performance using GSM Measurement DataGSM Measurement Data (cont’d) (cont’d)

• Investigate the propagation characteristics (shadowing components)

• Estimate the cross-correlation coefficient of shadowing fadings.

• Estimate the correlation distance of shadowing fadings.

• The measurements data were applied for simulations of the proposed handover algorithm.

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Verifying Performance using Verifying Performance using GSM Measurement DataGSM Measurement Data (cont’d) (cont’d)

24[15] D.B. Lin, R.T. Juang, H.P. Lin, and C.Y. Ke, “Mobile Location Estimation Based on Differences of Signal Attenuations for GSM Systems,” Proc. IEEE Soc. Int’l Conf. Antennas and Propagation, vol.1, pp. 77-80, June 2003.

Verifying Performance using Verifying Performance using GSM Measurement DataGSM Measurement Data (cont’d) (cont’d)

• the proposed handover algorithm reduces the number of handovers (18-26%) and only slightly increases the signal outage probability

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Conclusion Conclusion

• An improved handover algorithm for suppressing the ping-pong effect in cellular systems is verified by the GSM measurement data.– estimating the velocity of the mobile station based on non-

GPS location techniques – Low computational complexity, and – no database or lookup table is required.

• The simulations indicate that the number of unnecessary handovers can be reduced 18-26%, while the signal outage probability remains similar.

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