1. Location Management in Cellular Networks: Classification of the Most Important Paradigms, Realistic Simulation Framework, and Relative Performance Analysis Author: K. Kyamakya, Klaus Jobmann IEEE Transactions on Vehicular Technology, Vol. 54, No. 2, Mar. 2005 Speaker: Jun Shen

2. Overview Background Motivation Contribution Methodology Strength and Drawback of the paper Link with the class Link with project Q&A

3. Background Mobile network is more and more popular Increasing number of mobile subscribers the emergence of different mobile communication technology e.g. IEEE 802.11 WLAN, 3G/4G wireless cellular network, bluetooth, Everything on the move, e.g. laptop, PDA, mobile

4. Motivation (1) Cellular network is one of most important network in daily life, almost every mobile network is based on cellular network, GSM, CDMA, UMTS, X-CDMA How to lower the cost of system management and control scheme?

5. Motivation (2) Location management (LM) is one important part of management and control scheme, one good start point There are lots of location management schemes presented, what is the most efficient one?

6. Contribution Classification of published location management methods Presents results of a related extensive performance comparison of various location management in cellular network- --LM with profile is most efficient scheme

7. Methodology--Some Assumptions(1) LM scheme cost components Paging Polling cycle Number of cells polled Location update Mobility pattern Call pattern Overlook the impact of handover because it focus on radio mobility This paper focus on signaling cost only

8. Methodology--Some Assumptions(2) Network architecture

9. Methodology overview Define&Study a universal structure of a performance analysis framework for LM methods Introduce&Impl. a realistic user mobility model and simulation environment A systematic comparative performance analysis of a representative sample of most important LM schemes.

10. Methodology—Current LM Overview LM scheme components Paging (cost are polling cycle and number of polling cells sensitive) Polling cycle---one ~ three polling cycles (with delay constraint) Polling area---static/dynamic based on profile Location update Static LA– cost depends on topology Dynamic LA --- cost depends on user mobility and call pattern

11. Methodology—Overview of PA

12. Methodology—Overview of LU

13. Methodology—Mobility Model (1)

14. Methodology—Mobility Model (2) The paper adopts: Activity-based approach stress user mobility More realistic Consider the impact of aggregate traffic on individual behavior Generate reference mobility profile used to develop a Markov model with history

15. Methodology—Mobility Model (3) The model includes: Space dimension accuracy of street segment data can be obtained from roadmap (e.g. GPS roadmap) Commercial simulation tool available--VISUM Simulation of aggregate traffic state profile Location, timing and sequencing of individual user movement

16. Methodology—Mobility Model (4) The activity-based model includes: Number of activities of interest for a user Time zone for each activity Activity duration profile Activity sequence profile Geographic location of activities

17. Methodology—Mobility Model (5) The activity-based model

18. Methodology—Mobility Model (6) User classification:

19. Methodology-Sample LM method (1) Profile classification:

20. Methodology-Sample LM method (2) LM classification (to be continued):

21. Methodology-Sample LM method (3) LM classification:

22. Methodology-Sample LM method (3) Brief Introduction (to be continued): GSM classic : same as textbook mentioned GSM+profile : allow sequential paging rather than blanket paging Scourias: use profile to dynamically setting up the LA for a user SCOUKYA: Enhancement of Scourias adopt GSM+profile fallback method Reduce dependence on movement history LA has a predefined max size

23. Methodology-Sample LM method (4) Brief Introduction (to be continued): Movement-based: refer to textbook Distance-based: refer to textbook Direction-based: LU whenever movement direction changed Direction-based sector method: use a sector of direction instead of a single direction

24. Methodology-Sample LM method (5) Brief Introduction : SCOUKYA2: replace type2 profile with type3 one BIEST: Use type4 profile Iteratively increase the size of LA(according to profile) until cost of paging > cost of update BIEST_KYA: Use type3 profile LA of fixed size KYAMA: LA-based + timer-based Macro LA—actual LA + next LA

25. Methodology-Simulation context (1) Overall scheme (to be continued): DB part

26. Methodology-Simulation context (2) Overall scheme: functional structure

27. Methodology-Simulation context (3) Geographical and aggregation data: From the administration of town Hannover From the traffic planning of the university of Hannover Radio cell structure: square size, cell diameter range [100m, 7km]

28. Methodology-Simulation context (4) Timing of user movement (contd) Activity location: C1-C7: data from the Hannover admin. C8,9: random distribution over the city Activity sequencing: C1-C7: data from survey C8,9: random transition and duration matrix

29. Methodology-Simulation context (5) Possible values for the duration, two groups:

30. Methodology-Simulation context (6) Call arrival profile Fix call numbers per day Distribute numbers over a day

31. Methodology-performance analysis (1) Mobility characterization—simplify the designed model Develop two metrics and a benchmark – for the purpose of comparison

32. Methodology-performance analysis (2) Mobility Simplificaiton (one example) ---- contd Cell dwell time: independent of any activity duration and transition matrix if consider logarithmic axes

33. Methodology-performance analysis (3) Elements of interest Average activity duration Activity location randomly distributed over the geographical surface Activity transition matrix can be taken random Radius of geographical area is R Average call intensity Average CHT Average cell size

34. Methodology-performance analysis (3) Performance analysis Call to mobility ratio (CTM) CTM=Avg number of calls per day/ Avg activity duration *100 A indicator of user activity determinism Cost = nPA + c* nLU nPA: average number of paging nLU: average number of locaion update C: nLU/nPA, [5,10]

35. Methodology-performance analysis (4) Performance analysis (c=5)

36. Methodology-performance analysis (5) Performance analysis (c=10)

37. Methodology-performance analysis (6) Performance analysis

38. Link between paper and class? This paper gives a thorough review of current LM scheme It gives an extension of standard LM scheme.

39. How the paper is related to my project? The paper show a way to evaluate the efficiency of LM scheme My project is to compare the efficiency of two LM scheme