Lecture Notes

SES # TOPICS LECTURE NOTES
1

Signing Up

First Reading Assignment

Lecture #1

(PDF)
2

Channels

Capacity and Mutual Information

(PDF)
3

Analysis of Repetition Code Meta-channel

Capacity of Meta-channel

Prior, Extrinsic, Posterior and Intrinsic Probabilities

(PDF)
4

Prior, Extrinsic and Posterior Probabilities, II

Normalizing Constants

Example: Symmetric Channels

Decoding Codes

Example: Parity

(PDF)
5

Parity Continued
The Gaussian Distribution

The Gaussian and Erasure Channels

The Parity Product Code

BER

Heuristic Decoding of the Parity Product Code

Confidence Intervals

How big should N be?

Plotting in MATLAB®

(PDF)
6

Introduction

Two Variables

Simplifying Computations

Three Variables

Trees

(PDF)
7

Markov Property

Simplifying Probability Computation

(PDF)
8

Vector Spaces

Duals of vector spaces

Codes and Matrices

(PDF)
9

LDPC Codes

Decoding

SNR, dB

(PDF)
10 In-class debugging session
11

Belief Propagation on Trees

Dynamic Programming

Infnite Trees

Small Project 2

(PDF)
12

Representing Probabilities, Equality Nodes

Representing Probabilities, Parity Nodes

(PDF)
13

The Binary Erasure Channel

Analysis of LDPC on BEC

Making the Analysis Rigorous on Trees

Using the Polynomials

Capacity Estimation, Revisited

(PDF)
14

Convolutional Codes

Trellis Representation

Decoding Convolutional Codes

(PDF)
15

Remarks on Convolutional Codes

Turbo Codes

Decoding

Exit Charts

(PDF)
16

Decoding Modules

Final Projects

(PDF)
17

Developments in Iterative Decoding

Achieving Capacity on the BEC

Encoding

Density Evolution

Exit Charts, Revisited

Why we use bad codes to make good codes?

(PDF)