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19E033SPT - Random processes in telecommunications

Course specification
Course title Random processes in telecommunications
Acronym 19E033SPT
Study programme Electrical Engineering and Computing
Module Information and Communication Technologies - Audio and Video Technologies, Information and Communication Technologies - Internet and Mobile Communications, Information and Communication Technologies - Microwave Technology, Telecommunications and Information Technologies - Audio and Video Technologies, Telecommunications and Information Technologies - Information and Communication Technologies, Telecommunications and Information Technologies - Microwave Technology
Type of study bachelor academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
    Lecturer/Associate (for OTC)
    ESPB 6.0 Status elective
    Condition no prerequisite
    The goal A systematic explanation of the application of random processes in telecommunication signals analysis and communication system performance estimation. During the computer exercises, students will learn basic facts about random processes while principles of statistical theory of telecommunications will be highlighted.
    The outcome Provide students with the ability to understand basic probabilistic methods applicable for telecommunications. The applications of the presented methods and principles in simulation analysis of telecommunications systems will be considered in more details.
    Contents
    URL to the subject page https://teams.microsoft.com/l/team/19%3Arj1SBYLtuB1QcuFn2uuz4U61Zhzigol7bRt8qBq2FQ41%40thread.tacv2/conversations?groupId=e592d585-efb0-438a-8576-14d36a5ddf2f&tenantId=1774ef2e-9c62-478a-8d3a-fd2a495547ba
    Contents of lectures Random signals. Random variables, random processes. Central limit theorem. Ensemble, stationarity, ergodicity. Autocorrelation function. Software generation of random processes. Wiener-Khintchin theorem. Autoregressive models, prediction. Yule-Walker equations. Wiener filter. Statistical decision (MAP, ML). Statistical learning and inference and basic machine learning techniques.
    Contents of exercises Exercises and laboratory exercises.
    Literature
    1. D. Drajic, Introduction to Statistical Communications Theory, 2nd ed., Academic Mind, 2006.
    2. A. Papoulis, Probability, Random Variables, and Stohastic Processes, 2nd ed., McGraw Hill Book Company, New York, 1986. (Original title)
    3. R. Gallager, Stochastic Processes: Theory for Applications, Cambridge University Press, 2013. (Original title)
    4. R. V. Hogg, E. A. Tanis, D. L. Zimmerman, Probability and Statistical Inference, 9th ed., Pearson Education, Inc., 2015. (Original title)
    5. S. Miller, D.Childers, Probability and Random Processes: With Applications to Signal Processing and Communications, 2nd ed., Elsevier, 2012. (Original title)
    Number of hours per week during the semester/trimester/year
    Lectures Exercises OTC Study and Research Other classes
    3 1 1
    Methods of teaching Lectures, exercises, laboratory exercises, homeworks.
    Knowledge score (maximum points 100)
    Pre obligations Points Final exam Points
    Activites during lectures 0 Test paper 30
    Practical lessons 70 Oral examination 0
    Projects 0
    Colloquia 0
    Seminars 0