Course detail
Analysis of Radiocommunication Signals
FEKT-MPC-ARSAcad. year: 2025/2026
The proposed structure of the subject focuses on the use of selected mathematical techniques in modern communication signal processing and wireless communication theory. The goal is to present students specialized mathematical apparatus, which is essential to understanding the principles of modern wireless communications.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Department
Entry knowledge
Rules for evaluation and completion of the course
Aims
Students after completing the course should be able to solve problems associated with verification and testing assumptions and properties of the investigated phenomena and data files in the telecommunications field. The student is able to: (a) quantifying the probability of the event; (b) distinguishing between the random variables and describe their characteristics; (c) to test the hypothesis; (d) analyse and describe measurements; (e) estimating the shape of the spectrum and identify the spectral components; (f) identify and test the presence of a signal in noise; (g) evaluate the classification and construct the ROC curve.
Study aids
Prerequisites and corequisites
Basic literature
STEHLÍKOVÁ, B., TIRPÁKOVÁ, A., POMĚNKOVÁ, J., MARKECHOVÁ, D. Metodologie výzkumu a statistická inference. 9. vyd. Brno: Folia univ. agric. et silvic. Mendel. Brun., 2009. II. ISBN 978-80-7375-362-7. (CS)
Recommended reading
Classification of course in study plans
- Programme MPC-EKT Master's 1 year of study, winter semester, compulsory-optional
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
1. Introduction to probability theory. 2. Random variable. 3. Central limit theorem. 4. Random vectors. 5. Estimation: theory and applications 6. Random processes I. 7. Random processes II. 8. Correlation of stochastic signals 9. Spectra of stochastic signals 10. Criteria and parameter estimation. 11. Detectors and classification. 12. Detection of signals hidden in noise. 13. Gaussian mixture models. PCA.
Exercise in computer lab
Teacher / Lecturer
Syllabus
1. Introduction to the course 2. Probability in examples 3. Discrete RV modelling 4. Modeling continuous RV 5. CLV 6. Estimation and testing in Matlab 7. Testing random processes 8. Correlation of stochastic processes 9. Spectrum estimation of stochastic processes 10. ROC curve 11. Detectors and detection 12-13. Individual project presentation