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 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) |
