Dynamic Bandwidth Reservation in Cellular Networks Using Road Topology Based Mobility Predictions InfoCom 2004

1. Dynamic Bandwidth Reservation inCellular Networks Using Road Topology Based Mobility PredictionsInfoCom 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 Forced termination is worse than blocking a new call !! Insufficient bandwidth Forced termination i.e., handoff “dropped” • Prioritize handoffs by reserving bandwidth • Tradeoff  more news calls blocked

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

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 KnowingRoad Topology Candidate Cell A Candidate Cell B Handoff regions Reserve more in Cell A! Probability 0.1 Probability 0.9 Where to reserve bandwidth?

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

11. All segments Handoff-probable segments only 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

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

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

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

15. Prediction Database Update Procedure(1/3)

16. Prediction Database Update Procedure (2/3)

17. 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 Two processes: 1) Compute Rtarget periodically: using predictions falling within the next T 2) Adapt T : to achieve desired PFT arrival time t0 t0+T departure T PFT

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 Time T Rtarget increases monotonically with T 2 1 0 time 1 T PFT Sum of bandwidth changes Control PFT by adjusting T Peak=1 1 Set Rtarget to peak 0 time 1

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. Under-reservation occurs when predicted order is reversed Use prediction limits to introduce biases Choose L & U experimentally [ctarget, w, tLPL(L), tUPL(U)]

27. Adjusting Tthreshold at each BS

28. Adjusting Tthreshold at each BS

29. Adjusting Rtarget 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 nextcell & 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

34. 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