1. RANSim: Simulating a DS-CDMA Fading Channel with Traffic that Arrives in Bursts Colette Consani Heidi Proske

2. Content Background of Cellular Network Theory Introduction Design Simulator Input and Output Pseudo-Random Number Generation Colette’s Work Conclusion

3. Background: Cellular Networks A cell consists of: a Node B many User Equipment (UE) Focus: The downlink channel of a single cell

4. Background: DS-CDMA Multiple users share the same spectrum/channel simultaneously Individual Channels are multiplied by orthogonal pseudo-noise (PN) codes Channels are bundled together and transmitted over radio link Decode received signal with user-specific PN code, ideally removing the effect of other channels

5. Background: Interference The performance of a cellular network is limited by 2 factors: – Multiple Access Interference Low-level background interference – Multipath Fading Ricean or Rayleigh

6. Background: Interference The performance of a cellular network is limited by 2 factors: – Multiple Access Interference Low-level background interference – Multipath Fading Ricean or Rayleigh

7. Introduction: Problem Definition Jesse Landman is developing a Hidden Markov Model (HMM) to analyse trends for a DS-CDMA fading channel with traffic that arrives in bursts. Produce simulated performance results of a DS- CDMA fading channel with bursty traffic arrivals so that they can be compared to the results produced by the above-mentioned analytic model.

8. Introduction: Requirements Create a GUI Allow the user to specify certain inputs as pseudo-random numbers generated according to statistical distributions. – For e.g., geometrically distributed packet sizes Create a Simulation Engine (SE) to produce the required performance metrics.

9. Introduction: Roadmap of Work Jul AugSep MarJun Oct Reading Design: UML GUI Prototype Implementation: Statistical Distributions Result File Parsing Plot graphs GUI Testing: GUI Random Number Results Documentation

10. Design: System Abstraction

11. Design: Use Cases

12. Design: GUI Prototype

13. Implementation: GUI Walkthrough 1/4

14. Implementation: GUI Walkthrough 2/4

15. Implementation: GUI Walkthrough 3/4

16. Implementation: GUI Walkthrough 4/4

17. Implementation: Pseudo-Random Numbers Needed to model several aspects of pseudo- randomness: packet size (size in bits of data to be transmitted) inter arrival time of packets (the time between consecutive packet arrivals at the Node B) interfering signals

18. Testing: Pseudo-Random Numbers

19. RANSim Overview

20. Overview Simulation Engine: – Background – Design – Implementation – Testing Results Comparison Conclusion

21. RANSim Roadmap Jul Mid Aug End Sep MarJun Reading SE Testing Documentation Mid Oct ReadingDesign Model Conceptualisation Model Translation Output Analysis Implementation GUI & Distributions Implementation GUI Testing Documentation

22. Simulation Theory Developing a simulator: 1. Create a conceptual model 2. Translate the conceptual model into code To be confident in the results, a simulator needs to be Validated Verified Background

23. Simulator Validity “There is no such thing (for a simulation model) as ‘the test for validity’. Rather there have developed certain empirical guidelines and sets of tests. The simulator follows the guidelines and conducts applicable tests in the process of developing the model in order to build up his or her confidence. Validation of a simulation study is a continuous process that begins from the start of the study. Confidence is built into the model as the study proceeds. It is not just something done at the end.” “Discrete System Simulation”, Bulgren, 1982 Background

24. Developing a Valid Simulator nine techniques for developing valid models – Law, 2001 structured development steps – Banks, 2001 formal statistical procedures for output analysis Background

25. Developing a Valid Simulator nine techniques for developing valid models – Law, 2001 structured development steps – Banks, 2001 formal statistical procedures for output analysis Background

26. Techniques applied to promote validity 1. Formulating the Problem Precisely 2. Interviewing Subject-Matter Experts 3. Interacting with the Decision-Maker on a Regular Basis Background

27. Techniques applied to promote validity 1. Formulating the Problem Precisely 2. Interviewing Subject-Matter Experts 3. Interacting with the Decision-Maker on a Regular Basis 4. Using Quantitative Techniques to Validate Components of the Model 5. Documenting the Conceptual Model 6. Performing a Structured Walk-Through of the Conceptual Model Background

28. Techniques applied to promote validity 1. Formulating the Problem Precisely 2. Interviewing Subject-Matter Experts 3. Interacting with the Decision-Maker on a Regular Basis 4. Using Quantitative Techniques to Validate Components of the Model 5. Documenting the Conceptual Model 6. Performing a Structured Walk-Through of the Conceptual Model 7. Performing Sensitivity Analyses to Determine Important Model Factors 8. Using Graphical Plots and Animations of the Simulation Output Data 9. Validating the Output from the Overall Simulation Model Background

29. Developing a Valid Simulator nine techniques for developing valid models – Law, 2001 structured development steps – Banks, 2001 formal statistical procedures for output analysis Background

30. Development Steps Legend Design Implementation Testing Documentation Background

31. Developing a Valid Simulator nine techniques for developing valid models – Law, 2001 structured development steps – Banks, 2001 formal statistical procedures for output analysis Background

32. Output Data Analysis Background

33. RANSim Roadmap Jul Mid Aug End Sep MarJun Reading SE Testing Documentation Mid Oct ReadingDesign Model Conceptualisation Model Translation Output Analysis Implementation GUI & Distributions Implementation GUI Testing Documentation

34. Simulation Engine Components Design

35. Conceptual Model – Traffic Generator Design

36. Conceptual Model – Base Station Design

37. Conceptual Model – Radio Channel Design

38. Conceptual Model – User Equipment Design

39. Design: Issue “A common mistake of the inexperienced is to try to build a highly-detailed model right from the start.” “A Guide to Simulation”, Bratley, 1983

40. RANSim Roadmap Jul Mid Aug End Sep MarJun Reading SE Testing Documentation Mid Oct ReadingDesign Model Conceptualisation Model Translation Output Analysis Implementation GUI & Distributions Implementation GUI Testing Documentation

41. Model Translation Implementation Traffic Generator Base Station Radio Channel User Equipment

42. Model Translation – Traffic Generator Markov Modulated Poisson Process: Inter-Arrival Time: Packet Size: Implementation Traffic Generator Base Station Radio Channel User Equipment High Load Low Load

43. Model Translation – Base Station Packet Processing Slot Scheduling Implementation Traffic Generator Base Station Radio Channel User Equipment T5T3T2 T4T3T1 T3T2 Frame Slot Queues Packet Slot

44. Model Translation – Radio Channel probability of a bit error on the channel amplitude of received signal, inverse of Implementation Traffic Generator Base Station Radio Channel User Equipment

45. Model Translation – User Equipment Slot retransmissions Packet retransmissions Raw data logging Implementation Traffic Generator Base Station Radio Channel User Equipment Packet Number Slot Number Frame Number Start Time End Time Number of Interfering Users Offered Load Error Flag Error Info

46. Output Analysis - Non-overlapping Batch Means X X 1 X 2 X 3 X 4 Batch Mean Simulation Mean Simulation Variance Simulation Confidence Interval Implementation

47. Output Analysis - Graphical Representation Implementation

48. RANSim Roadmap Jul Mid Aug End Sep MarJun Reading SE Testing Documentation Mid Oct ReadingDesign Model Conceptualisation Model Translation Output Analysis Implementation GUI & Distributions Implementation GUI Testing Documentation

49. Verification Used 10 different techniques to test the implementation Examples: – Flow diagrams & then stepping through the code – Time plots of certain aspects of the simulation engine – fixed input for the traffic arrival rates of the interfering users Testing

50. Traffic Generation Testing Testing

51. Interference Testing Testing

52. Success Story

53. Recap - Problem Definition Produce simulated performance results of a DS- CDMA Fading Channel with Bursty Traffic Arrivals so that they can be compared to the results produced by the markov model developed by Landman. Success Story

54. Results Comparison Success Story

55. Recap - Problem Definition Produce simulated performance results of a DS- CDMA Fading Channel with Bursty Traffic Arrivals so that they can be compared to the results produced by the markov model developed by Landman. Success Story

56. Conclusion The performance results from RANSim and the markov model correlated sufficiently to increase the level of validity of the markov model Success Story

57. Discussion Holding you ransom until you have asked your questions on our