Information regarding our 2023/24 admissions cycle

Our 2023/24 postgraduate taught admissions cycle will open on Monday, 10 October. For most programmes, the application form will not open until this date.

MSc Communications and Signal Processing with Extended Research

Year of entry: 2023

Course unit details:
Introduction to Communication and Signal Analysis

Course unit fact file
Unit code EEEN60481
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
Offered by
Available as a free choice unit? No

Overview

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.    

 

Aims

This course unit detail provides the framework for delivery in the current academic year and may be subject to change due to any additional Covid-19 impact.  Please see Blackboard / course unit related emails for any further updates.

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.


 

 

Learning outcomes

On the successful completion of the course, students will be able to:

Developed

Assessed

ILO 1

Apply the Fourier methods to signals and systems, and Infer the interplay between time and frequency domains

X

X

ILO 2

Apply the Nyquist sampling theorem to low-pass and band-pass signals and systems.

X

X

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.

X

X

ILO 4

Apply Monte-Carlo simulation methods to model the random behaviour of communication systems and evaluate their average performance.

X

X

ILO 5

Identify key properties of discrete and continuous stochastic processes and apply them to solve communication and signal processing problems.

X

X

ILO 6

Apply statistical tools to derive optimal estimators and detectors for signal processing.

X

X

 

Teaching and learning methods

Lectures: 30 hours

Tutorials/problems classes: 6 hours

 

Assessment methods

Method Weight
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.

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
Lectures 30
Tutorials 6
Independent study hours
Independent study 114

Teaching staff

Staff member Role
Khairi Hamdi Unit coordinator

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