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Validation of an improved location-based handover algorithm using GSM measurement data

IEEE Transactions on Mobile Computing, Vol. 4, No 5, Sep. 2005. Validation of an improved location-based handover algorithm using GSM measurement data. Hsin-Piao Lin; Rong-Terng Juang; Ding-Bing Lin. Outline. Introduction Proposed Handover Algorithm

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Validation of an improved location-based handover algorithm using GSM measurement data

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  1. IEEE Transactions on Mobile Computing, Vol. 4, No 5, Sep. 2005 Validation of an improved location-based handover algorithm using GSM measurement data Hsin-Piao Lin; Rong-Terng Juang; Ding-Bing Lin

  2. Outline • Introduction • Proposed Handover Algorithm • Analysis of Handover Performance with Location Errors • Verifying Performance Using GSM Measurement Data • Conclusion

  3. Introduction • 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.

  4. Introduction (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

  5. Introduction (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

  6. Introduction (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

  7. System 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

  8. System Model (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) [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.

  9. System Model (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.

  10. Proposed 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.

  11. Proposed Handover Algorithm (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.

  12. Proposed Handover Algorithm (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

  13. Proposed Handover Algorithm (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 σu2 and

  14. Proposed Handover Algorithm (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

  15. 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.

  16. omnidirectional antenna • Simulation using SignalPro by EDX Engineering • includes a set of planning tools for wireless communication system The height of each BS is 35 m the 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 =0.1 V = 30 km/h, ρT=0.85 handover alarm threshold = -80 dBm handover margin = 6dB 1.4 KM 1.6 KM

  17. Analysis of Handover Performance with Location Errors • 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] 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.

  18. 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

  19. 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

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

  21. Verifying Performance using GSM Measurement Data • The proposed handover algorithm was applied to a living GSM system (1,800MHz) in urban Taipei city. 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. 1.6 KM 2.1 KM

  22. Verifying Performance using GSM Measurement Data (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.

  23. Verifying Performance using GSM Measurement Data (cont’d) [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.

  24. Verifying Performance using GSM Measurement Data (cont’d) • the proposed handover algorithm reduces the number of handovers (18-26%) and only slightly increases the signal outage probability

  25. 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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