13M031STT - Statistical Communication Theory

Course specification
Course title Statistical Communication Theory
Acronym 13M031STT
Study programme
Type of study master academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
    ESPB 6.0 Status elective
    Condition no prerequisites
    The goal To provide students with understanding of statistical signal analysis in communications. Introduction to filtering, correlation and detection theory. The applications of the presented concepts in design of communication systems and big data analysis.
    The outcome At the end of the course, the students will be familiar with the basic methods that use probabilistic approach for solving communications problems. The application of presented concepts in communication systems performance analysis will be given. Optimal decision and pattern recognition in big data sets will be considered also.
    Contents of lectures Distributions and transformations of random variables. Characteristic function. Correlation and covariance matrix. Main components extraction, singular value decomposition. Estimation, prediction and detection. Detection in MIMO systems, space-time codes. Regression analysis. Data analytics, the application of matrix methods in pattern recognition.
    Contents of exercises Exercises and homeworks
    1. D. Drajic, Introduction in Statistical Communication Theory, Academic Mind, 2nd ed., Belgrade, 2006.
    2. D. Middleton, An Introduction to Statistical Communication Theory, McGraw-Hill Book Company, New York, 1958. (2nd reprint Ed., IEEE Press, New York 1996.) (Original title)
    3. H. L. Van Trees, Detection, Estimation, and Modulation Theory, Part I: Detection, Estimation, and Linear Modulation Theory, John Wiley & Sons, Inc., New York, 2001. (Original title)
    4. T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2009. (Original title)
    5. P. Moulin, V. Veeravalli, Statistical Inference for Engineers and Data Scientists, Cambridge University Press, 2018. (Original title)
    Number of hours per week during the semester/trimester/year
    Lectures Exercises OTC Study and Research Other classes
    3 1
    Methods of teaching Lectures, exercises, homeworks, project (optionally).
    Knowledge score (maximum points 100)
    Pre obligations Points Final exam Points
    Activites during lectures 0 Test paper 40
    Practical lessons 60 Oral examination 0
    Colloquia 0
    Seminars 0