Error-Correcting Codes Laboratory

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)