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A Hierarchical Position Prediction Algorithm for Efficient Management of Resources in Cellular Networks

A Hierarchical Position Prediction Algorithm for Efficient Management of Resources in Cellular Networks . 1. 2. 3. Tong Liu, Paramvir Bahl, Imrich Chlamtac. 1. Tellabs Wireless Systems Division Microsoft Research Erik Jonsson School of Engineering and Computer

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A Hierarchical Position Prediction Algorithm for Efficient Management of Resources in Cellular Networks

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  1. A Hierarchical Position Prediction Algorithm for Efficient Management of Resources in Cellular Networks 1 2 3 Tong Liu, Paramvir Bahl, Imrich Chlamtac 1 Tellabs Wireless Systems Division Microsoft Research Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas 2 3 GLOBECOM ‘97, November 1997

  2. Main Messages … mobility prediction is a promising technique for improving resource efficiency and connection reliability in cellular networks. … theoretical richness of stochastic signal processing field makes it feasible for predicting random intercell movement… Approximate pattern matching Extended, self-learning Kalman Filtering Bi-level stochastic movement model Intercell movement prediction

  3. Outline • Mobility prediction - Problem and Framework • Related work in literature • Hierarchical Position Prediction • User Mobility Model - A Global View • Approximate Pattern Matching • User Mobility Model - A Local View • Extended, Self-learning Kalman Filtering • Prediction Performance • Conclusions

  4. ? ? Mobility Prediction - Problem Description Global Prediction: Next-cell(s) Crossing Local Prediction: Dynamic State

  5. ? Mobility Prediction - Problem Description

  6. Improve lifetime connectivity and radio resource efficiency - Bandwidth Reservation - QoS Control - Optimal Routing - Position/velocity Based Handoff Movement Observation Cell Geometry Movement Model Prediction Algorithm Speed Position Time Cell site Mobility Prediction - Framework Global Global Local Local

  7. Historical Movement Pattern Recently Crossed Cells Pattern Matching Related Work in Literature Next-cell prediction based on movement pattern Prediction Performance Next Cell Tabbane (JSAC, 1995) Liu and Maguire (ICUPC,1995) Liu , Munro and Barton (ICUPC, 1996)

  8. Current Cell ID Cell Transition Probability Matrix Look Up Table Related Work in Literature Next-cell prediction based on Markov Chain model Prediction Performance Next Cell Bar-Noy, Kessler and Sidi (Jour. Of Wireless Networks, 1995) Akyildiz and Ho(Proc. ACM SIGCOMM, 1995) Liu and Maguire (ICUPC,1995)

  9. Recent Crossed Cells RSS Measurement Pattern Template Linear Dynamic System Cell Geometry Approximate Pattern Matching Extended Self-Learning Kalman Filter Position Speed Prediction of Random Intercell Movement Prediction Performance Next Cells

  10. b3 b2 b3 b4 b2 b3 b4 b3 b1 a b4 User Mobility Model - A Global View User Mobility Pattern Editing Process inserting User Actual Path changing deleting UAP Editing Operation Spatial Cost UMP UAP b1 Insertion b2 b3 b1 a b1 deletion b3 b4 b1 b2 b2 b1 change

  11. User Mobility Model - A Global View Spatial Cost

  12. Approximate Pattern Matching

  13. S S 1 m User Mobility Model - A Local View Measurement noise S U(t) Moving Dynamics + 2 a(t) F( ) + r(t) Nonlinear measurement Time correlated random acceleration Commands P( a(t)/Sm ) P( a(t)/S1 ) P( a(t)/S2 ) -Amax S2 Sm Amax S1

  14. Dynamic Equations Continuous-time dynamic equation: Discrete-time dynamic equation:

  15. Observation Model d2 d1 d0

  16. Adaptive Dynamic State Estimator

  17. Adaptive Dynamic State Estimator

  18. Recursive Algorithm

  19. Prediction of Next Cell cell 1 cell 6 cell 2 Direction cell 0 cell 3 cell 5 Trajectory cell 4

  20. Approximate Pattern Matching User Profile Hierarchical Position Prediction Global Prediction Global Prediction User Mobility Buffer size:L UAP Forming Dynamic state Local Prediction Local Prediction of Next Cell Optimum Adaptive Filtering RSS measurement

  21. Movement Pattern 2 Movement Pattern 1 ---Uncrossed Cell ---Crossed Cell Significance of Local Prediction A practical situation necessitates looking-ahead mode for movement pattern identification

  22. Prediction Performance - Simulation Parameters

  23. Prediction Performance - Trajectory Tracking

  24. Prediction Performance - Speed Estimation

  25. Local Prediction of Next Cell

  26. Local Prediction of Next Cell Parametric Behavior of Next-cell Prediction

  27. Local Prediction of Next Cell

  28. Current Cell d(UAP,UMP1) d(UAP,UMP2) Global Prediction C9 C8 2 2 C9 2 C10 C18 C17 C16 C10 3 C17 C16 Global Prediction Determine Edit Distance: UMP1 UMP2 1 1 UAP UAP 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 3 4 5 6 7 1 2 3 4 1 1 1 0 1 2 3 4 5 6 1 0 1 2 3 1 1 2 3 4 5 6 1 0 1 2 2 1 2 3 4 5 1 2 3 4 5 3 2 2 Prediction Result:

  29. Conclusion • Hierarchical Movement Model • Top level: Movement Pattern subject to random editing operations • Bottom level: A linear dynamic system driven by the combination of a semi-Markovian process and Color Gaussian Noise. • Hierarchical Position Prediction Algorithm • Approximate Pattern Matching • Extended Self-learning Kalman Filter

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