Communications Systems Modelling

Introduction and the need of CSM via simulation

The importance of CSM is described and the challenges of performance evaluation of communications systems are discussed. The focus is on the methods for performance evaluation and the simulation approaches. The hierarchical model for communications networks is also introduced and a set of computer software technologies is reviewed.

• Methods of performance evaluation
• Hierarchical view of communications networks
• The application of simulation to the design of communication systems

Basics of simulation and modelling methodology

Problem-solving techniques using simulation are introduced and the relevant modelling concepts are discussed. Error sources in simulation and some practical issues are also described.

• Methodology of problem solving for simulation
• Basic concepts of modelling
• Performance Evaluation Techniques
• Error sources in simulation
• Simulation environment and software issues

Representation of signals and systems in simulation

The analytical tools are reviewed to understand and characterise signals and systems that are important for simulation.

• Deterministic signals and systems
• Continuous and discrete-time signals
• Signals in the time and frequency domains
• Fourier series and transforms
• Convolution
• Probability and stochastic processes
• Discrete and fast fourier transform (DFT/FFT)
• Hilbert transform and the complex envelope
• Sampling, signal design and analysis

Modelling and simulation of LTI systems

Modelling and simulation of linear systems are discussed in detail.

• Analogue filter design using Butterworth and Chebyshev filters
• Conversion method from analogue to digital filters (impulse invariant method, matched-Z transform and bilinear transformation)
• Frequency transformations for filter design
• Approximate continuous time structures in discrete time for simulation
• Linear-phase FIR filter design
• Windowing effects and optimisation
• Simulation of filtering with IIR filters
• Simulation of filtering with FIR filters
• Time-varying linear systems in the time and frequency domains

Modelling and simulation of nonlinear systems

Concepts used to model and analyse nonlinear systems are introduced.

• Bandwidth estimation for nonlinear systems
• Memoryless baseband nonlinearities
• Power series analysis
• Memoryless bandpass nonlinearities
• Nonlinearities with memory
• Volterra series modelling

Simulation and generation of random numbers

We describe the techniques to generate independent and correlated random sequences for simulation of noises and random communications sequences.

• Principle of Monte Carlo simulation
• Random number generation
• Generation of uniform random numbers
• Generating random numbers from a PDF
• Generating Gaussian random variables
• Generating independent random sequences
• Generating correlated random sequences

Case study: OFDM wireless networks

• Modelling for wireless channels
• Multi-ray frequency selective fading channels
• OFDM systems
• SER and BER simulations

Network modelling and queueing theory

• Stochastic processes (e.g. Poisson process)
• Markov chains
• Little’s theorem
• Birth-ceath process
• M/M/1 queue
• Demonstration of NS-2 simulation