1. Dynamic Bandwidth Reservation in Cellular Networks Using Road Topology Based Mobility Predictions InfoCom 2004 Speaker : Bo-Chun Wang 2004.4.21

2. Outline 。 Motivation 。 Relative work 。 Road topology based mobility prediction 。 Dynamic bandwidth reservation scheme 。 Simulations and results

3. Motivation Prioritize handoffs by reserving bandwidth Tradeoff  more news calls blocked Forced termination is worse than blocking a new call !! Forced termination i.e., handoff “dropped” Insufficient bandwidth

4. Motivation Handoff arrivals are random –Dynamic reservation more efficient No knowledge of future: use prediction –Accuracy   reservation efficiency  time Reservation Static: P FT = 0.01, P CB = 0.15 Dynamic: P FT = 0.01, P CB = 0.10 P FT = Forced termination probability P CB = New call blocking probability

5. Outline 。 Motivation 。 Relative work 。 Road topology based mobility prediction 。 Dynamic bandwidth reservation scheme 。 Simulations and results

6. Relative Work Signal Strength Mobility(direction,speed) History=>probability(user,BS)

7. Shortcoming in Previous Work Assumes hexagonal/circular boundary –Actual cell boundary fuzzy & irregularly shaped Road topology information not utilized –Could potentially yield better accuracy

8. Outline 。 Motivation 。 Relative work 。 Road topology based mobility prediction 。 Dynamic bandwidth reservation scheme 。 Simulations and results

9. Advantages of Knowing Road Topology Candidate Cell A Candidate Cell B Where to reserve bandwidth? Probability 0.1 Probability 0.9 Handoff regions Reserve more in Cell A!

10. Preliminaries Each BS keeps a database of the roads within its coverage area –Roads are divided into “ road segments ” –Topology extracted from digital maps A B Road segment (A,B)

11. Database Entries For each road segment: –Neighboring segments –Transition probability to each neighbor –Statistical data: Transit time Probability of handoff Time spent before handoff Handoff locations Target handoff cell All segments Handoff-probable segments only

12. Modeling Segment-transition Transition between road segments modeled as 2nd order Markov processes A B C D E F MT1 MT2 I J A B C D E F MT1 MT2 I J MT1 & MT2 have different probabilities of entering EF

13. Prediction Output [c target, w, t LPL (  L ), t UPL (  U )] Predicted target handoff cell Prediction weight Upper prediction limit Lower prediction limit Time t LPL (  L ): P [actual handoff time  t LPL (  L )] =  L Time t UPL (  U ): P [actual handoff time  t UPL (  U )] =  U 4-tuple: Derived using previously observed prediction errors

14. Prediction Output [c target, w, t LPL (  L ), t UPL (  U )] 4-tuple: (One for each possible path to each handoff region) c target :Target cell if handoff occurs on EF w:P(AB  BE  EF, handoff at EF) t LPL (  L ), t UPL (  U ): Prediction limits of time from handoff if AB  BE  EF occurs B C D E F Handoff region A w pdf of time from handoff Can have multiple 4-tuples per MT

15. Prediction Database Update Procedure(1/3)

16. Prediction Database Update Procedure (2/3)

17. Prediction Database Update Procedure (3/3) Prediction Database Update Procedure (3/3)

18. Prediction Algorithm(1/3)

19. Prediction Algorithm(2/3)

20. Prediction Algorithm(3/3)

21. Outline 。 Motivation 。 Relative work 。 Road topology based mobility prediction 。 Dynamic bandwidth reservation scheme 。 Simulations and results

22. Reservation Scheme time t0t0 t0+Tt0+T arrival departure Two processes: 1) Compute R target periodically: using predictions falling within the next T 2) Adapt T : to achieve desired P FT T   P FT 

23. Logic Behind the Scheme Suppose: Have precise handoff information Question: How much bandwidth should we reserve to prevent any incoming handoff from being dropped within T ?

24. Perfect Knowledge Over T Bandwidth change due to incoming/ outgoing handoffs 2 1 0 11 Time T 1 0 11 Sum of bandwidth changes Peak=1 R target increases monotonically with T T   P FT  time Set R target to peak Control P FT by adjusting T

25. A More Realistic Scenario Previous example assumes perfect knowledge of handoff timings Examine a more realistic scenario: only predictions available –Prediction errors in handoff timings

26. [c target, w, t LPL (  L ), t UPL (  U )] Use prediction limits to introduce biases Under-reservation occurs when predicted order is reversed Choose  L &  U experimentally

27. Adjusting T threshold at each BS

29. Adjusting R target at each BS

30. Outline 。 Motivation 。 Relative work 。 Road topology based mobility prediction 。 Dynamic bandwidth reservation scheme 。 Simulations and results

31. Simulation Model 19 wireless cells Randomly generated roads Uncertain handoff regions Traffic lights Capacity = 100 BUs Voice (1 BU) & video (4 BUs) calls

32. Other Schemes for Comparison Benchmark: knows MT ’ s next cell & handoff time Static: fixed reservation target Reactive: reacts to forced terminations Choi ’ s AC1: uses MT ’ s previous cell, & time in current cell LE: linear extrapolation (Infocom ’ 01) RTB with Path Knowledge (RTB_PK): knows future path

33. Simulation Result

35. Summary Mobility predictions incorporate both positioning info & road topology knowledge –No cell geometry assumption Adaptive reservations use both incoming & outgoing handoff predictions Prediction accuracy, reservation efficiency –Lesser new call blocking while meeting handoff prioritization target