1. Presentation CS 5214 Pin Lu Bill Ward “Performance analysis of cellular mobile communication networks supporting multimedia services”.

2. Agenda Background Information Description of Model Results generated by the Model

3. Goals of the paper: 1. Present an analytical model that simulates a communication network that provides integrated services to a population of mobile users 2. Validate the model based on performance results 3. Use results to assess the Quality of Service. Background

4. Authors: M. Ajmone MarsanFull Professor at the Electronics Dept, Politecnico di Torino, Italy S. MaranoProf. of Telecommunications Networks, University of Calabria, Italy C. Mastroianni PhD, University of Calabria, Italy M. MeoPhD, Politecnico di Torino, Italy Publication: Mobile Networks and Applications 5 (2000) pp. 167-177 Sponsors: Italian National Research Council Italian Ministry for University and Scientific Research Background Continued

5. Background: History Stages of mobile communications: –Previous technical approach: Increase capacity available for voices services. –Current technical approach: Increase capacity available for voice and provide a wider spectrum of telecommunication services; multimedia service (includes data and images).

6. – Simulation Preferred when detailed behavior of a specific area is needed. – Analytical Model Be able to obtain more general results. – Combination of both. Evaluation of cellular telecommunication networks

7. – R. Steele & M. Nofal (1992) [8] Analyzed performance of personal communication network based on microcells covering city street. – K.K. Leung, W.A. Massey & W. Whitt (1994) [4] Fluid model to describe wireless system – D. Hong, & S. Rappaport (1986) [2] Techniques to reduce forced terminations due to handover failure. – R. Beraldi, S, Marano, & C. Mastroianni (1997) [1] Hierarchical model of cellular system based on microcells and macrocells. Previous Research

8. Model Description Analytical Model is based on:  Continuous time Multi-dimensional Birth- Death Process Descriptive Stages of the Model 1. Assumptions and parameters 2. State Definition and model driving processes 3. Flow balance equations and computation of equilibrium state probabilities 4. Evaluation of aggregate performance measures.

9. – Cellular TeleCom provides 3 classes service Class A – voice only Class B – data only Class C – voice and data(Multimedia) – Number of radio channels available – N Class A – requires 1 channel Class B – requires C d channels Class C – requires C d +1 channels Assumptions and parameters

10. – The occurrence of a Handover Failure is worse than blocking a new call. The Proposed Solutions are: 1. Add channels specifically for handovers  Add C h channels 2. Decouple Multimedia calls  Class D: require 1 channel – The Analytical Model focuses on a single cell. Cell’s behavior is isolated from other cells Possible limitation: non-Homogenous cells. Assumptions and parameters (con’t)

11. – Assumptions for a Single cell New Call request arrivals are Poisson point processes. Handover request arrival processes from adjacent cells are Poisson point processes. The dwell time of a Mobile Terminal is a random variable with a negative exponential pdf. The unencumbered session duration is a random variable with a negative exponential pdf. Assumptions and parameters (con’t) Cell A Cell C Cell B Dwell time

12. Cell state = # of currently active connections for each class of traffic: s=(v,d,m,r) v - # of active voice calls in cell d - # of active data calls in cell m - # of multimedia calls in cell r - # of active voice components of a decoupled multimedia call n ( s )- the function giving the total # of channels allocated to active connections when cell is in state s n ( s ) = v + d C d + m (C d + 1) + r State Definition

13. – Driving processes produce different kinds of events that must be processed by the network: requests of new voice, data and multimedia calls incoming handover requests for voice, data and multimedia outgoing handover requests for voice, data and decoupled calls completion of voice, data, multimedia and decoupled calls Driving Processes Cell A Cell C Cell B

14. – For each driving process, it is possible to determine what are the possible successor states of s=(v,d,m,r) New Call Requests are accepted if the # of free channels, excluding those reserved for handovers, is such that the call can be accommodated. if there are no active Class D connections (ie. r = 0) Flow Balance Equations Add Voice Example: n=99, N =100, Ch=0, Cd=8  Initial state: (91,1,0,0) (99  100 – 0 – 1) ^ r = 0 Successor state: (92,1,0,0)

15. Incoming handover requests for voice or data are accepted if: there are enough free channels to accommodate the request in the cell. Incoming handover requests for multimedia calls (whether decoupled or not), have 2 possible successor states. Flow Balance Equations (con’t) decoupled voice only Handover Add Mmedia Example: n=99, N =100,  Ch=0, Cd=8 : Initial state: (91,1,0,0) 100 - (8 + 1) < 99  100 – 1 Successor state: (91,1,0,1)

16. Completion of Calls and Outgoing handover requests. Have the effect of freeing some channels in the cell. in some cases, this leads to the recombination of a decoupled multimedia call. Flow Balance Equations (con’t) underline on decoupled multimedia calls that are recombined Complete 1 Mmedia add 2 MM Example: n=93,  N =100, Ch=0, Cd=8 : Initial state: (82,0,1,2) 2 >1  (93 – (8 +1)  100 – 2*8 Successor state: (82,0,2,0)

17. The transition rate from state s to a state k is computed by summing the contributions resulting from the driving processes. The state space size depends on the values of C d, r max, and the # of radio channels available in the cell, N. The max # of active calls possible: Voice: V=N Data: D=N/C d  Multimedia: M =  N/(C d + 1) State space size is upper bounded by: (V + 1)(D + 1)(M+1)(r max + 1) Model Complexity

18. Four performance indices will be used in the presentation of the results: the average carried traffic. the new call blocking probability. the handover failure probability. the probability of recovering a decoupled multimedia call. Definition of transition rate(event rate). The mean frequency of events that cause a state transition, where an event can be a new call attempt, a handover request, or a call termination. Event rate R: Aggregate Performance Measures -q(s,s) is total transition out of state s p(s) is the steady-state probability of state s

19. AC: Defined as the average number of channels in use in the cell. Average Carried Traffic n(s) is the # of busy channels in state s p(s) is the steady-state probability of state s Voice Average # of channels used for: Multimedia Data

20. PB: Defined as the average fraction of new call requests that cannot be satisfied by the cell base station, due to: lack of free channels to the presence of some decoupled multimedia connections waiting to be recombined. New Call Blocking Probability VoiceMultimedia Data The probability of a voice call being blocked is the least

21. PH: Defined as the average fraction of incoming handover requests that cannot be satisfied, causing the forced termination of the call. Incoming handover requests fail if the system is in one of the states belonging to subsets: Handover Failure Probability Voice Multimedia Data decoupling probability probability handover request of multimedia completely fails Hdec = {s: (N - C d -1 < n  N-1)  r < r max }

22. P rec : Defined as the probability that the data component of the decoupled multi- media call is resumed before the call is terminated by the user Probability of Recovering a Decoupled Multimedia Call R rec is relative rate of events that lead to recombination of a multimedia call R rec (s) is recombination rate r(s) is # of decoupled calls in state s ACr is the average number of decoupled calls in the cell

23. Results Comparisons between analytical and simulation results Explore alternate system configurations to assess their effectiveness.

24. Scenario - similar to ETSI (European Telecommunications Standards Institutes) Two classes of services –A (voice) and B (data) N = 64 Cd = 4 (8) Ch = 0 (4, 8) r max = Cd(0) 1/u v =1/u d =1/u m =100s 1/u hv =1/u hd =1/u hm =80s 75% voice call; 25% data call

25. Model Validation Simplify the model –Analyze one cell instead of whole network –Average incoming handover equals outgoing handover –New call generated according Poisson process –Call duration and dwell times according negative exponential distributions

26. Model Validation (Cont’d) Difference between analytical model and simulation – handle of handover – Analytical model simply balance in and out handover – Simulation provides a much detailed process about handover

27. Results show model and simulation are very similar to each other Fig. 1

28. Results show model and simulation are very similar to each other (Cont’d) Fig. 2

29. Above validation is based on f in = f out Expand model validation: f in = a*f out (a=0.6; 1.0; 1.4)

30. Simulation and analytical results for the average total carried traffic, with a=1.4 and a=0.6 Fig. 3

31. Simulation and analytical results for the average voice blocking probability, with a=1.4 and a=0.6 Fig. 4

32. Results: – The analytical model performance is still very accurate in comparison to numerical results obtained from simulation, although some loss of accuracy can be noted.

33. Performance Evaluation Class A and B services Class C service

34. Class A and B services – Previous numerical results are used – Number of channels allocated for each data call increased from Cd = 4 to Cd = 8 – Keep all other parameters same

35. 8 channels per data call is better than 4 channels when carrying 75% voice and 25% data Fig. 5

36. Higher numbers of data channels increases possibility of blocking new users Fig. 6

37. Results for class A and B: – Increasing the number of channels allocated to each data call while keeping the same call rate significantly increases the data offered traffic up to a point. – Beyond some point, system with 8 data channels offers worse carried traffic than with Cd=4. – Blocking probabilities for both data and voice for Cd=8 are much larger than Cd=4.

38. To minimize the difference in blocking probability between voice and data calls, a threshold Tv is introduced. – Number of free channels necessary for accepting new call - Tv – If number of busy channels > N – Tv; no voice call can be accepted.

39. The higher threshold, the better data traffic and the worse voice traffic Fig. 7

40. The higher the threshold, the higher probability that new voice call will be blocked; no significant impact on data blocking Fig. 8

41. Results of using threshold on voice: – This approach leads to better performance for data traffic, at the expense of a performance loss for voice traffic.

42. Class C services –Assumption: 60% voice call 20% data call 20% multimedia call Cd = 8 for data connection Number of channels reserved for handover calls (Ch) Number of busy channels > Ch, new calls are blocked

43. Adding handover channels reduces probability of handover failure for both data and voice Fig. 9

44. Results of adding handover channels: – Handover failure probability decreases with the increase of Ch Voice connection – –big decrease when Ch increased from 0 to 4, –minor decrease when Ch changed from 4 to 8, Data connection – –biggest decrease when increasing Ch from 4 to 8

45. Decoupling of multimedia calls – Multimedia call must be decoupled if available channel is not sufficient for both component but only for voice n > N – Cd –1 and n <= N –1 - Multimedia call will complete fail if no channel is free n = N

46. Higher #Ch reduces probability that call will be decoupled and reduces probability that call will fail during handover Fig. 10

47. Results: – Reduction of handover failure probability is significant when Ch increased from 0 to 4 – Minor change when Ch changed from 4 to 8 – No extraordinary change for decoupling probability when Ch changes

48. The more channels assigned to handover, the higher the probability that the call will be recombined Fig. 11

49. Results: – The lower call rate, the higher recombination probability – The higher Ch, the higher recombination probability

50. Access effectiveness of multimedia decoupling approach (Two situations) (1) Cd = 4 and Ch = 0, rmax = Cd (2) Cd = 4 and Ch = 0, rmax = 0

51. Using decoupling approach reduces probability of handover failure Fig. 12

52. Results: – Decoupling approach allows the reduction of the handover failure probability by a factor between 5 and 10, depending on the cell traffic load

53. Conclusion Model was based on continuous-time multidimensional birth-death process Focuses on one cell only Improvement 1: Using handover channel improves network performance Improvement 2: Decoupling multimedia calls improves network performance