1. A Hierarchical Position Prediction Algorithm for Efficient Management of Resources in Cellular Networks Tong Liu, Paramvir Bahl, Imrich Chlamtac 2 1 3 Tellabs Wireless Systems Division Microsoft Research Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas 1 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… Bi-level stochastic movement model Approximate pattern matching Extended, self-learning Kalman Filtering 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. Mobility Prediction - Framework Movement Observation Movement Model Cell Geometry Prediction Algorithm Position Speed Cell siteTime Improve lifetime connectivity and radio resource efficiency - Bandwidth Reservation - QoS Control - Optimal Routing - Position/velocity Based Handoff Global Local Global Local

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

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

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

10. User Mobility Model - A Global View User Mobility Pattern Editing Process inserting changing deleting User Actual Path UAP Editing Operation UMP b1b1 b2b2 b3b3 b1b1 b2b2 b3b3 a UAP Spatial Cost Insertion deletion change b1b1 b3b3 b4b4 b2b2 b1b1 b3b3 b4b4 b1b1 b2b2 b3b3 b3b3 b4b4 b4b4 b1b1 b2b2 a

11. User Mobility Model - A Global View Spatial Cost

12. Approximate Pattern Matching

13. User Mobility Model - A Local View S 1 S m S 2 Moving Dynamics Commands Time correlated random acceleration r(t) + U(t) a(t) F( ) Measurement noise Nonlinear measurement -A max S1S1 S2S2 SmSm A max P( a(t)/S 1 )P( a(t)/S 2 ) P( a(t)/S m ) +

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

15. Observation Model d0d0 d1d1 d2d2

16. Adaptive Dynamic State Estimator

18. Recursive Algorithm

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

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

21. Significance of Local Prediction Movement Pattern 2 Movement Pattern 1 ---Crossed Cell ---Uncrossed Cell 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. Parametric Behavior of Next-cell Prediction

27. Local Prediction of Next Cell

28. Global Prediction d(UAP,UMP1) 2 2 2 Current Cell C8C8 C9C9 C 10 d(UAP,UMP2) 3 Global Prediction C9C9 C 10 C 18 C 17 C 16 C 17 C 16 Determine Edit Distance: Prediction Result: UAP UMP1 1 00000 01234 1 0 1 1 1 123 0123 102 3 UAP UMP2 1 00000 0 1234 1 0 1 1 1 21 012 21 000 567 456 456 2345 2 2 3 4 5 3

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