Author: K. Kyamakya, Klaus Jobmann

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