CS249 Digital Communications and Signal Processing
CS24915 Digital Communications and Signal Processing
Introductory description
The aim of the module is to acquaint students with the principles and practice of digital communications  from the fundamental basis of communication to how signals are represented and processed.
This module is only available to students in the second year of their degree and is not available as an unusual option to students in other years of study.
Module aims
The aim of the module is to acquaint students with the principles and practice of digital communications  from the fundamental basis of communication to how signals are represented and processed.
The module develops an analytical approach to problems in communication design and operation, grounded in elements of communication theory sufficient to give students an understanding of the problems that affect its reliability and efficiency.
It introduces the theory and implementation of digital signal processing approaches, including the representation of signals in communication systems, filtering techniques and the applications of digital signal processing.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
Information Sources and Coding: Information theory, coding of information for efficiency and error protection;
Data transmission: Channel characteristics, signalling methods, interference and noise, synchronisation, data compression and encryption;
Signal Representation: Representation of discrete time signals in time and frequency; z transform and Fourier representations; discrete approximation of continuous signals; sampling and quantisation; stochastic signals and noise processes;
Filtering: Analysis and synthesis of discrete time filters; finite impulse response and infinite impulse response filters; frequency response of digital filters; poles and zeros; filters for correlation and detection; matched filters;
Digital Signal Processing applications: Processing of speech signals using digital techniques.
Learning outcomes
By the end of the module, students should be able to:
  Understand the structure of the communication process.
  Explain the main control issues in communication networks.
  Understand the principles of digital signal processing and have a knowledge of its main areas of application.
  Design, implement and analyse the behaviour of simple digital signal processing algorithms.
Indicative reading list
Please see Talis Aspire link for most up to darte list.
View reading list on Talis Aspire
Subject specific skills
At the end of the course students should be able to:
calculate the information content and entropy of a random variable from its probability distribution;
relate the entropies of variables in terms of their probabilities;
construct efficient codes for data on communication channels;
understand the concept of digital signals;
understand encoding and communication schemes in terms of the spectral properties of signals;
describe compression schemes, and efficient coding using Fourier Transform and other representations for data.
Transferable skills
At the end of the course students should be able to:
Using MatLab to work on other problems related to mathematics
Have a better understanding of advanced mathematics;
Equipped with basic knowledge to work on other areas such as audio, video and in general big data processing;
Applications in other sciences: genomics; neuroscience; astrophysics; noisy signal classification; and pattern recognition including biometrics.
Study time
Type  Required 

Lectures  30 sessions of 1 hour (20%) 
Seminars  10 sessions of 1 hour (7%) 
Private study  110 hours (73%) 
Total  150 hours 
Private study description
There are many online materials useful for our module such as textbooks for machine learning and in general you should read:
 https://en.wikipedia.org/wiki/Digital_signal_processing
 Hari Krishna Garg: Digital Signal Processing Algorithms, CRC Press, ISBN 0849371783
 P. Gaydecki: Foundations Of Digital Signal Processing: Theory, Algorithms And Hardware Design, Institution of Electrical Engineers, ISBN 0852964315
 Ashfaq Khan: Digital Signal Processing Fundamentals, Charles River Media, ISBN 1584502819
 Sen M. Kuo, WoonSeng Gan: Digital Signal Processors: Architectures, Implementations, and Applications, Prentice Hall, ISBN 0130352144
 Paul A. Lynn, Wolfgang Fuerst: Introductory Digital Signal Processing with Computer Applications, John Wiley & Sons, ISBN 0471979848
 Richard G. Lyons: Understanding Digital Signal Processing, Prentice Hall, ISBN 0131089897
 Vijay Madisetti, Douglas B. Williams: The Digital Signal Processing Handbook, CRC Press, ISBN 0849385725
 James H. McClellan, Ronald W. Schafer, Mark A. Yoder: Signal Processing First, Prentice Hall, ISBN 0130909998
 Bernard Mulgrew, Peter Grant, John Thompson: Digital Signal Processing – Concepts and Applications, Palgrave Macmillan, ISBN 0333963563
 Boaz Porat: A Course in Digital Signal Processing, Wiley, ISBN 0471149616
 John G. Proakis, Dimitris Manolakis: Digital Signal Processing: Principles, Algorithms and Applications, 4th ed, Pearson, April 2006, ISBN 9780131873742
 John G. Proakis: A SelfStudy Guide for Digital Signal Processing, Prentice Hall, ISBN 0131432397
 Charles A. Schuler: Digital Signal Processing: A HandsOn Approach, McGrawHill, ISBN 0078297443
 Doug Smith: Digital Signal Processing Technology: Essentials of the Communications Revolution, American Radio Relay League, ISBN 0872598195
 Smith, Steven W. (2002). Digital Signal Processing: A Practical Guide for Engineers and Scientists. Newnes. ISBN 075067444X.
 Stein, Jonathan Yaakov (20001009). Digital Signal Processing, a Computer Science Perspective. Wiley. ISBN 0471295469.
 Stergiopoulos, Stergios (2000). Advanced Signal Processing Handbook: Theory and Implementation for Radar, Sonar, and Medical Imaging RealTime Systems. CRC Press. ISBN 0849336910.
 Van De Vegte, Joyce (2001). Fundamentals of Digital Signal Processing. Prentice Hall. ISBN 0130160776.
 Oppenheim, Alan V.; Schafer, Ronald W. (2001). DiscreteTime Signal Processing. Pearson. ISBN 1292025727.
 Hayes, Monson H. Statistical digital signal processing and modeling. John Wiley & Sons, 2009. (with MATLAB scripts)
 David MacKay Information Theory, Inference, and Learning Algorithms (Hardback, 640 pages, Published September 2003)
 https://www.cl.cam.ac.uk/teaching/1920/InfoTheory/
 Cover, T.M. & Thomas, J.A. (2006). Elements of information theory. New York: Wiley.
In addition, students should:
 Review lecture notes
 Complete coursework
 Revise for examinations
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Students can register for this module without taking any assessment.
Assessment group D2
Weighting  Study time  

Programming assignment (Coursework)  20%  
Oncampus Examination  80%  
CS249 exam  A paper which examines the course content and ensures learning outcomes are achieved. ~Platforms  AEP

Assessment group R1
Weighting  Study time  

Online Examination  100%  
CS249 resit examination ~Platforms  AEP

Feedback on assessment
Feedback in seminars
Courses
This module is Core for:
 Year 2 of UCSAG400 BSc Computing Systems
 Year 2 of UCSAG402 MEng Computing Systems
This module is Optional for:
 Year 2 of UCSAI1N1 Undergraduate Computer Science with Business Studies
 Year 2 of UCSAG406 Undergraduate Computer Systems Engineering
 Year 2 of UCSAG408 Undergraduate Computer Systems Engineering
 Year 2 of UCSAG5N1 Undergraduate Computer and Management Sciences
 Year 2 of USTAG302 Undergraduate Data Science
 Year 2 of USTAG304 Undergraduate Data Science (MSci)
This module is Core option list B for:
 Year 2 of UCSAG5N1 Undergraduate Computer and Management Sciences
This module is Option list A for:
 Year 2 of UCSAG500 Undergraduate Computer Science
 Year 2 of UCSAG503 Undergraduate Computer Science MEng
This module is Option list B for:
 Year 2 of UCSAG4G1 Undergraduate Discrete Mathematics
 Year 2 of UCSAG4G3 Undergraduate Discrete Mathematics
Further Information
Term 2
15 CATS (7.5 ECTS)
Note: This module is only available to students in the second year of their degree and is not available as an unusual option to students in other years of study.