| 1. | Course Title | Intelligent Communication Technologies | |||||||||||
| 2. | Code | 4ФЕИТ10009 | |||||||||||
| 3. | Study program | 11-IBS, 12-KIT | |||||||||||
| 4. | Organizer of the study program (unit, institute, department) | Faculty of Electrical Engineering and Information Technologies | |||||||||||
| 5. | Degree (first, second, third cycle) | Second cycle | |||||||||||
| 6. | Academic year/semester | I/1 | 7. | Number of ECTS credits | 6.00 | ||||||||
| 8. | Lecturer | Dr Zoran Hadji-Velkov | |||||||||||
| 9. | Course Prerequisites | ||||||||||||
| 10. | Course Goals (acquired competencies):
Understanding of methods for modeling, simulation and design of modern communication systems. Knowledge of machine learning techniques and bringing intelligence to resource management of wireless systems. Knowledge of modern technologies for machine-type communication. Ability to apply optimization methods and tools in the design of wireless systems. |
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| 11. | Course Syllabus:
Modeling and simulation methods for telecommunications. Performance evaluation of communication receivers. Methods and tools for solving optimization problems in wireless communications. Design, optimization and resource management of wireless systems. Machine learning and artificial intelligence in telecommunications, machine learning techniques, distributed machine learning, federated and reinforcement learning. Machine type communication and Internet of things. |
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| 12. | Learning methods:
Lectures, preparation of seminar paper, solving project and standalone exercises, independent work |
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| 13. | Total number of course hours | 180 | |||||||||||
| 14. | Distribution of course hours | 3 + 3 | |||||||||||
| 15. | Forms of teaching | 15.1 | Lectures-theoretical teaching | 45 hours | |||||||||
| 15.2 | Exercises (laboratory, practice classes), seminars, teamwork | 45 hours | |||||||||||
| 16. | Other course activities | 16.1 | Projects, seminar papers | 30 hours | |||||||||
| 16.2 | Individual tasks | 30 hours | |||||||||||
| 16.3 | Homework and self-learning | 30 hours | |||||||||||
| 17. | Grading | ||||||||||||
| 17.1 | Exams | 0 points | |||||||||||
| 17.2 | Seminar work/project (presentation: written and oral) | 50 points | |||||||||||
| 17.3. | Activity and participation | 0 points | |||||||||||
| 17.4. | Final exam | 50 points | |||||||||||
| 18. | Grading criteria (points) | up to 50 points | 5 (five) (F) | ||||||||||
| from 51 to 60 points | 6 (six) (E) | ||||||||||||
| from 61 to 70 points | 7 (seven) (D) | ||||||||||||
| from 71 to 80 points | 8 (eight) (C) | ||||||||||||
| from 81 to 90 points | 9 (nine) (B) | ||||||||||||
| from 91 to 100 points | 10 (ten) (A) | ||||||||||||
| 19. | Conditions for acquiring teacher’s signature and for taking final exam | Regular attendance of classes / consultations | |||||||||||
| 20. | Forms of assessment |
Written exam and preparation of a seminar paper / project assignment. The written exam has a maximum duration of 120 minutes, during which the use of books, scripts, manuscripts or notes is allowed. |
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| 21. | Language | Macedonian and English | |||||||||||
| 22. | Method of monitoring of teaching quality | Self-evaluation | |||||||||||
| 23. | Literature | ||||||||||||
| 23.1. | Required Literature | ||||||||||||
| No. | Author | Title | Publisher | Year | |||||||||
| 1. | S. Shalev-Shwartz and S. Ben-David | Understanding Machine Learning | Cambridge University Press | 2014 | |||||||||
| 2. | M. C. Jeruchim, P. Balaban, and K. S. Shanmugan | Simulation of Communication Systems | Kluwer Academic Publishers (2nd ed) | 2002 | |||||||||
| 23.2. | Additional Literature | ||||||||||||
| No. | Author | Title | Publisher | Year | |||||||||
| 1. | Reuven Y. Rubinstein and Dirk P. Kroese | Simulation and the Monte Carlo Method | Wiley2007 | 2007 | |||||||||
