[{"command":"insert","method":null,"selector":".course-modal-ajax-placeholder-962045","data":"\u003Cdiv class=\u0022container content-rte\u0022\u003E\n  \u003Cdiv class=\u0022row\u0022\u003E\n    \u003Cdiv class=\u0022box col-md-6 bg-primary text-white\u0022\u003E\n      \u003Ch2 class=\u0022h1 text-left\u0022\u003E  Information and Coding Theory\n\u003C\/h2\u003E\n                  \u003Cdiv class=\u0022field-spacing\u0022\u003E\n        \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EECTS :\n          6\n        \u003C\/div\u003E\n      \u003C\/div\u003E\n                  \u003Cdiv class=\u0022field-spacing\u0022\u003E\n        \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ESemester:\n          Semestre 1\n        \u003C\/div\u003E\n      \u003C\/div\u003E\n                    \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ELangue d\u0026#039;enseignement\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003EAnglais\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EEnseignement \u00e0 distance\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003Enon\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n      \u003C\/div\u003E\n    \u003Cdiv class=\u0022box col-md-6\u0022\u003E\n            \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EPr\u00e9requis\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003EAnalysis, linear algebra and probability at undergraduate level\u003C\/p\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EProgramme \/ plan \/ contenus\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003ECOURSE OUTLINE\u003C\/p\u003E\n\u003Cp\u003EPart I\u0026nbsp;\u003Cbr\u003E\n1. Properties of Shannon\u0027s Information Measures\u003Cbr\u003E\n2. Markov Chain, Fundamental Inequalities and Entropy of Stationary Sources\u003Cbr\u003E\n3. Lossless Source Coding\u003Cbr\u003E\nWeak Typical Sequences, Noiseless Source Coding and its Coding Theorem,\u003Cbr\u003E\n4. Method of Types\u003Cbr\u003E\nType Counting Lemma, Strong Typical Sets, Delta Sequences, etc.\u003Cbr\u003E\n5. Coding Theorem for Noisy Channels (Binary Symmetric Channels, Gaussian Channels (AWGN), etc.)\u003Cbr\u003E\n6. Coding Theorem for Lossy Source Coding\u003Cbr\u003E\n7. Converse to the Coding Theorems for Discrete Memoryless Sources (DMSs) and Channels (DMCs)\u003Cbr\u003E\n8. Capacity of Wireless Channels\u003Cbr\u003E\nChannels with Colored Gaussian Noise and Fading, Channels with Multiple Antennas (MIMO) and Fading, etc.\u003C\/p\u003E\n\u003Cp\u003EPart II\u0026nbsp;:\u0026nbsp;\u003C\/p\u003E\n\u003Cp\u003E1-Introduction to error-correcting codes\u0026nbsp;\u003C\/p\u003E\n\u003Cp\u003E2-Linear cyclic codes\u0026nbsp;\u003C\/p\u003E\n\u003Cp\u003E3-Linear convolutional codes\u0026nbsp;\u003C\/p\u003E\n\u003Cp\u003E4-Performance of linear codes under MLD\u0026nbsp;\u003C\/p\u003E\n\u003Cp\u003E5-Factor graphs and the sum-product algorithm\u0026nbsp;\u003C\/p\u003E\n\u003Cp\u003E6-Sparse-graph codes\u0026nbsp;\u003C\/p\u003E\n\u003Cp\u003E7-Sparse-graph codes\u0026nbsp;\u003C\/p\u003E\n\u003Cp\u003E8- Sparse-graph codes\u003C\/p\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EObjectifs d\u0026#039;apprentissage\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003EThis is a graduate-level introduction to the fundamental ideas and results of information theory and coding. It is composed of two parts, Information Theory and Coding Theory, and does not assume prior study in information theory. It is intended for graduate students from mathematics, engineering or related areas wanting a good background in fundamental and applicable information theory. In part I, the course discusses elementary measures and properties of information at a more sophisticated level. Then, method of Types and the main results of Shannon\u2019s theory, mainly the coding theorems for source and channel coding, are discussed. Afterwards, the course touches on topics that are explored more fully in later courses, converses, capacity of wireless channels, etc. In part II,\u0026nbsp;the course focuses on coding theory. We remind students of the basics of the algebraic coding theory for conventional binary-input output-symmetric memoryless channels. Then, sparse graph codes will be covered. Code construction aspects, iterative decoding, and mathematical tools for design optimization are reviewed in detail. Then, the course expounds the principles of non-binary coding for the Gaussian channel and shows how and why coded modulations can also benefit from sparse-graph codes optimized for binary-input channels and iterative decoding.\u003C\/p\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n                                \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EBibliographie\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003E[1] COVER, T.M., and THOMA S, J.A., Elements of Information Theory, Wiley, 1991.\u0026nbsp;\u003C\/p\u003E\n\u003Cp\u003E[2] CSISZAR, I., and KORNER, J., Information Theory: Coding Theorems for Discrete Memoryless Systems, Academic Press, 1997.\u0026nbsp;\u003C\/p\u003E\n\u003Cp\u003E[3] GOLDSMITH, A., Wireless Communications, Cambridge University Press, 2005.\u0026nbsp;\u003C\/p\u003E\n\u003Cp\u003E[4] TSE, D., VISWANATH, P., Fundamentals of Wireless Communications, Cambridge University Press, 2005.\u0026nbsp;\u003C\/p\u003E\n\u003Cp\u003E[5] GALLAGER, R.G., Information and Reliable Communications, Wiley, 1968.\u0026nbsp;\u003C\/p\u003E\n\u003Cp\u003E[6] BERLEKAMP, E., Algebraic Decoding Theory, McGraw Hill, 1968.\u0026nbsp;\u003C\/p\u003E\n\u003Cp\u003E[7] MAC WILLIAMS, F.J., SLOANE, N.J.A., Theory of Error-Correcting Codes, North Holland Publishing, 1977.\u0026nbsp;\u003C\/p\u003E\n\u003Cp\u003E[8] MCELIECE, R.J., Finite Fields for Computer Scientists and Engineers, Kluwer Academic Publishers, 1987.\u0026nbsp;\u003C\/p\u003E\n\u003Cp\u003E[9] VITERBI, A.J., OMURA, J.K., Principles of Digital Communications and Coding, McGraw Hill, 1979.\u003C\/p\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ENature de l\u0026#039;\u00e9valuation\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003EEvaluation Continue non Int\u00e9grale\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n      \u003C\/div\u003E\n  \u003C\/div\u003E\n\u003C\/div\u003E\n","settings":null}]