Course detail

Radiocommunication Signals

FEKT-MPA-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

English

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

doc. RNDr. Jitka Poměnková, Ph.D.

Department

Department of Radio Electronics (UREL)

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.

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.

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)

Classification of course in study plans

  • Programme MPC-EKT Master's 1 year of study, winter semester, compulsory-optional
  • Programme MPAJ-TEC Master's 1 year of study, winter semester, compulsory
  • Programme MPA-TEC Master's 1 year of study, winter semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. RNDr. Jitka Poměnková, Ph.D.

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

doc. RNDr. Jitka Poměnková, Ph.D.

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