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

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

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

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

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

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

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.

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.

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

Design: System Abstraction

Design: Use Cases

Design: GUI Prototype

Implementation: GUI Walkthrough 1/4

Implementation: GUI Walkthrough 2/4

Implementation: GUI Walkthrough 3/4

Implementation: GUI Walkthrough 4/4

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

Testing: Pseudo-Random Numbers

RANSim Overview

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

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

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

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

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

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

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

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

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

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

Development Steps Legend Design Implementation Testing Documentation Background

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

Output Data Analysis Background

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

Simulation Engine Components Design

Conceptual Model – Traffic Generator Design

Conceptual Model – Base Station Design

Conceptual Model – Radio Channel Design

Conceptual Model – User Equipment Design

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

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

Model Translation Implementation Traffic Generator Base Station Radio Channel User Equipment

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

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

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

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

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

Output Analysis - Graphical Representation Implementation

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

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

Traffic Generation Testing Testing

Interference Testing Testing

Success Story

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

Results Comparison Success Story

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

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

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