SP514E - Modern Channel Coding

Objectives

General objective:

At the end of this course, the students should be familiar with two types of modern channel codes, LDPC and TurboCodes. The students should be able to implement these codes in a communication signal and they should understand how their decoding methods works: iterative decoding algorithms. Finally, the students should be able to clearly understand the difference between classic and modern channel coding decoding methods.

Detailed objectives:

First, the student should understand the notion of Trellis Coded Modulation (TCM).


Second, the students should be familiar with the new notation given to define convolutional codes as well as with the new decoding method, BCJR algorithm, introduced in this course.


Third, the students should acquaint themselves with the TurboCodes: their definition, their fundamentals and their decoding method based on the BCJR algorithm. Moreover, the notion of intrinsic and extrinsic information should also be clearly understood.


Fourth, the students should be familiar with the concept of Factor Graphs and they should be able to use this concept together with the message-passing algorithm to solve marginal of multivariate functions. Moreover, they should be able to use this concept, or equivalently called Tanner graphs, to represent linear block channel codes.


Fifth, the student should be acquainted with the fundamentals of Low Parity Density Check (LDPC) channel codes and their decoding method, belief-propagation algorithm (or message-passing). The students should be able to represent LDPC channel codes using Tanner graphs.


Sixth and last, the students should be familiar with the concept of EXIT chart, which information is provided by this chart and how it is calculated.


Planning:


1. Trellis Coded Modulation (TCM)
2. Convolutional Codes 2
a. New notation
b. BCJR decoding algorithm
3. TurboCodes
a. Fundamentals
b. Decoding algorithm
4. Factor graphs and tanner graphs
5. LDPC channel codes
6. EXIT chart

Hours

• Lecture : 10h
• Supervised Practical Work : 2h