| 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 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) |