Course detail
Radiocommunication Signals
FEKT-MKA-ARSAcad. year: 2023/2024
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
Evaluation of activities is specified by a regulation, which is issued by the lecturer responsible for the course annually.
Aims
After completing the course, students should be able to independently solve problems associated with the verification and testing of assumptions and properties about the studied phenomena and data files in the telecommunications field. Furthermore, they should be able to independently solve practical tasks, ie choose and justify an appropriate method and apply it.
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
Recommended reading
KAY, S. Intuitive Probability and Random Processing using MATLAB, Springer 2005. (EN)
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. 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 course 2. Introduction to probability theory. 3. Discrete NV modelling. 4. Modelling continuous NV. 5. Relationships between distributions. 6. Testing in Matlab 7. Test II 8. Simulation of random processes 9. Correlation of stochastic signals 10. Spectra of stochastic signals 11. Detection of signals hidden in noise. 12 Test II 13 Course summing up