COURSE UNIT TITLE

: INFORMATION THEORY AND CODING

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
EEE 5075 INFORMATION THEORY AND CODING ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSISTANT PROFESSOR REYAT YILMAZ

Offered to

ELECTRICAL AND ELECTRONICS ENGINEERING NON -THESIS (EVENING PROGRAM)
ELECTRICAL AND ELECTRONICS ENGINEERING
ELECTRICAL AND ELECTRONICS ENGINEERING
ELECTRICAL AND ELECTRONICS ENGINEERING

Course Objective

The mathematical foundation for digital communications was established by Claude Shannon in 1948. In this pioneering work he formulated the basic problem of reliable transmission of information in statistical terms, using probabilistic models for information sources and communication channels. He adopted a logarithmic measure for the information content of a source and established basic limits on the maximum rate that digital information can be transmitted reliably over a communication channel.He showed for the transmission rate below channel capacity C, that the error probability averaged over all randomly selected codes can be made as small as desired. While this imlies the existence of good Codes, it left open the problem of designing such codes. Our aim in this course is first to present a brief introduction to information theory. In particular, the logarithmic measure adopted by Shannon to describe quantitatively the information content of a source will be described. Then the problem of a source encoding and provide, by means of example, several source encoding methods encountered in practice will be described. Next the attention will be turned to the transmission of the source output over a communication channel. The capacity of a channel will be defined.The course will continue with the channel coding, which is used to provide for the reliable transmission of digital information over the channel.

Learning Outcomes of the Course Unit

1   To be able to learn the concept of information.
2   To be able to define fundamental limits on the efficiency of a source and the rate of reliable information transmission over a channel.
3   To be able to use source coding techniques.
4   To be able to combine modulation techniques with channel coding.
5   To be able to propose different channel coding techniques for different channels.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Discrete Sources and Entropy
2 Source Coding techniques
3 Channel Models and Channel Capacity
4 Run-Length-Limited Codes
5 Linear Block Codes
6 Midterm Exam
7 Syndromes and Standart Array Decoding
8 Convolutional Codes
9 Viterbi Algorithm
10 Cyclic Codes
11 Trellis coded Modulation
12 Turbo code Structures
13 Turbo Decoding
14 Assignment Evaluation

Recomended or Required Reading

Textbook: Applied Coding and Information Theory for Engineers, Richard B. Wells, Prentice Hall,1999.
Supplementary Resources: Error-correcting Coding for Digital Communications, Clarke, G,C. and Cain, J.B., Plenum Press, 1983
Other materials: Course notes.

Planned Learning Activities and Teaching Methods

Lectures will be supported by regular homeworks.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 PRJ PROJECT
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE + PRJ/2 * 0.50 +FIN * 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE + PRJ/2 * 0.50 + RST * 0.50


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

reyad.yilmaz@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Preparation for midterm exam 1 8 8
Preparation for final exam 1 10 10
Preparing assignments 4 10 40
Preparations before/after weekly lectures 13 7 91
Midterm 1 3 3
Final 1 3 3
TOTAL WORKLOAD (hours) 194

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14PO.15
LO.121
LO.2211
LO.3111411
LO.4123111121
LO.53332411121