Course unit details:
Foundations of Communication Systems and Signal Analysis
Unit code | EEEN60481 |
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Credit rating | 15 |
Unit level | FHEQ level 7 – master's degree or fourth year of an integrated master's degree |
Teaching period(s) | Semester 1 |
Available as a free choice unit? | No |
Overview
Signal Analysis
Fourier transform and its properties. Autocorrelation and power spectrum. Sampling of low-pass and band-pass signals. Applications to the analysis and simulations of band-pass communication systems.
Probability and Random Signals:
Probability and random variables. Common Probability models. Vector random variables. Stochastic process and their properties. Random signals and their spectral characteristics. Response of linear System. Applications to communication systems and signal processing.
Estimation and Detection
Estimation and linear estimators. Hypothesis testing. Applications to communication systems and signal processing.
Simulation of Communication Systems
Monte-Carlo simulation techniques with applications to communication systems and networks.
Aims
To develop mathematical and simulation tools essential for the design and analysis of modern communication systems and networks
Appreciate the links between the various theoretical models and their practical applications to solve contemporary communication engineering and signal processing problems.
Application of the probability models and statistical methods to solve signal processing and communication engineering problems.
Learning outcomes
All ILOs are developed and assessed.
ILO 1 - Apply the Fourier methods to signals and systems, and Infer the interplay between time and frequency domains
ILO 2 - Apply the Nyquist sampling theorem to low-pass and band-pass signals and systems.
ILO 3 - Appraise the importance of probabilistic models for the design and analysis of communication systems and apply them to predict and evaluate the performance of communication systems and networks.
ILO 4 - Apply Monte-Carlo simulation methods to model the random behaviour of communication systems and evaluate their average performance.
ILO 5 - Identify key properties of discrete and continuous stochastic processes and apply them to solve communication and signal processing problems.
ILO 6 - Apply statistical tools to derive optimal estimators and detectors for signal processing.
Teaching and learning methods
Lectures: 30 hours
Tutorials/problems classes: 6 hours
Assessment methods
Method | Weight |
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Other | 20% |
Written exam | 80% |
Coursework: Matlab-based Monte-Carlo simulation exercise on a communication problem. Technical report that summarizes and criticises the simulation results. Worth 20% of the unit assessment.
Feedback methods
.
Recommended reading
Oppenheim, Signals, Systems and Inference, 2017.
Lapidoth, A foundation in digital communication, 2017
Fitz, Fundamentals of Communication Systems, 2007.
Miller, Probability and random processes with applications to signal processing and communications, 2012.
Ross, Introduction to probability models, 2019.
Leis, Communication Systems Principles Using MATLAB, 2018.
Dolecek, Random Signals and Processes Primer with MATLAB, 2013
Study hours
Scheduled activity hours | |
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Lectures | 30 |
Tutorials | 6 |
Independent study hours | |
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Independent study | 114 |
Teaching staff
Staff member | Role |
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Khairi Hamdi | Unit coordinator |