Course detail
Radiocommunication Signals
FEKT-MPA-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
English
Number of ECTS credits
5
Mode of study
Not applicable.
Guarantor
Department
Offered to foreign students
Of all faculties
Entry knowledge
A student who enrolls in the course should be able to compile a simple program in the Matlab environment and practice mathematical calculation procedures.
Rules for evaluation and completion of the course
Requirements for completion of a course are specified by a regulation issued by the lecturer responsible for the course and updated for every. Students will be evaluated by credit on the basis of gaining points in practice (max. 30 points, min. 15 points) and the final exam (max. 70 points, min. 35 points).
Evaluation of activities is specified by a regulation, which is issued by the lecturer responsible for the course annually.
Evaluation of activities is specified by a regulation, which is issued by the lecturer responsible for the course annually.
Aims
The aim of the course is to present to students a specialized mathematical-statistical apparatus, which is important for understanding the principles of modern wireless communication.
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.
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
Not applicable.
Prerequisites and corequisites
Not applicable.
Basic literature
KOBAYASHI, H. et al. Probability, random processes, and statistical analysis, Cambridge University Press, 2012. (EN)
Recommended reading
GOPI, E.S. Algorithm Collections for Digital Signal Processing Applications Using Matlab, Springer, 2007. (EN)
KAY, S. Intuitive Probability and Random Processing using MATLAB, Springer 2005. (EN)
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
26 hod., optionally
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
26 hod., compulsory
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