Random Processes and Information Theory

Course title: Random Processes and Information Theory

Code: FEIT10Z002

Number of credits (ECTS): 6

Weekly number of classes: 3+1+1+0

Institute: Telecommunications

Prerequisite for enrollment of the subject: None

Course goals/Competences: To get familiar with the characteristics of random signals, their autocorrelation function and spectral power density function. To learn the basic statistical model of information transmission and processing within communication systems.

Total available number of classes: 180

Curriculum: Random variable: definition, moments, types of probability distribution functions. Random vectors: basic concepts, moments, functions of random vectors. Elements of hypothesis testing: Bayes, MinMax, Neuman Pearson criteria. Random processes. Stationary and ergodic random processes. Autocorrelation functions and spectral power density. Transission of random processes through linear systems. Gaussian process. Poisson processes. White noice. Thermal noise. Enthropy, relative enthropy and transinformation. Memoryless sources, sources with memory. Enthropy of sources with memory (Markov sources). Source coding. Capacity of discrete memoryless channels; noisy-channel coding theorem. Gaussian channel and its capacity.

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