MSc Communications and Signal Processing with Extended Research

BRIEF DESCRIPTION OF THE UNIT:

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.   

The course unit 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.

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.

Lectures: 30 hours

Tutorials/problems classes: 6 hours

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.

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• Signals, Systems and Inference, Global Edition by Alan Oppenheim. Pearson, 2017.
• Fundamentals of Communications Systems by Michael Fitz. Cengage Learning, 1980.
• A foundation in digital communication by Lapidoth, Amos. Cambridge University Press, 2017.
• Communication systems principles using MATLAB  by Leis, John. John Wiley & Sons, Inc., 2018. 
• Communication systems engineering by Proakis, John G. Prentice Hall, 2002.
• Communication systems: an introduction to signals and noise in electrical communication Carlson, A. Bruce, 1937- McGraw-Hill Higher Education 2010.
• Communication systems by Haykin, Simon S. John Wiley, 2010. 
• Modern digital and analog communication systems. by Lathi, B. P. (Bhagwandas Pannalal). Oxford University Press, 2010. 
• Probability and stochastic processes: a friendly introduction for electrical and computer engineers by Yates, Roy D. John Wiley & Sons, 2014. 
• Random Signals and Processes Primer with MATLAB by Dolecek, Gordana Jovanovic. Springer New York, 2013. 
• Introduction to probability models by Ross, Sheldon M. Academic Press, an imprint of Elsevier, 2019.
• Probability and random processes: with applications to signal processing and communications by Miller, Scott L. Academic Press, 2012. 
• Probability, random processes, and statistical analysis by Kobayashi, Hisashi. Cambridge University Press, 2012. 
• Probability, random variables, statistics, and random processes: fundamentals & applications by Grami, Ali. Wiley, 2020. 
• Fundamentals of applied probability and random processes by Oliver Chukwudi. Academic Press, 2014. 
• Introduction to probability by Bertsekas, Dimitri P. Athena Scientific, 2008. 
• Introduction to probability: models and applications by Balakrishnan, N. Wiley, 2019.
• Stochastic simulation by Ripley, Brian D. Wiley, 1987.