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19D031VIR - Artificial Intelligence in Radio Communications

Course specification
Course title Artificial Intelligence in Radio Communications
Acronym 19D031VIR
Study programme Electrical Engineering and Computing
Module Telecommunications
Type of study doctoral studies
Lecturer (for classes)
Lecturer/Associate (for practice)
    Lecturer/Associate (for OTC)
      ESPB 9.0 Status mandatory
      Condition
      The goal Next-generation radio communication systems should support extremely large throughputs and radically new applications, what implies a new paradigm of implementation. The challenge is how to apply intelligent adaptive learning processes and decision-making methods in the next-generation networks. The application of artificial intelligence in all segments of the radio network is considered.
      The outcome Student who completes this course is expected to: * understands the basic concepts of various techniques of artificial intelligence, * understands the possibility of applying machine learning in different segments of a single radio system, * master the basic algorithms and software tools for applying the techniques of machine learning to specific problems.
      Contents
      Contents of lectures Overview of basic concepts of artificial intelligence and machine learning. Basic types of machine learning (unsupervised, reinforcement, supervised, semi-supervised). Classification, regression, clustering. Validation methods. Overfitting problem. Examples of applications from the radio system domain.
      Contents of exercises Independent practical research.
      Literature
      1. Machine Learning: Concepts, Methodologies, Tools and Applications, Information Resources Management Association, Information Science Reference, 2011. (Original title)
      2. Bishop C. M. (2006). Pattern recognition and machine learning. Springer Science + Business Media. (Original title)
      3. Shawe-Taylor J, Cristianini N. (2004). Kernel methods for pattern analysis. Cambridge University Press. (Original title)
      4. H. M. Hasoun, Fundamentals of Artificial Neural Networks, Massachusetts Institute of Technology (1995) (Original title)
      5. The MathWorks, Inc., „Matlab NN Toolbox Help,“ The MathWorks, Inc., http://www.mathworks.com/help/nnet/ref/nntool.html. (Original title)
      Number of hours per week during the semester/trimester/year
      Lectures Exercises OTC Study and Research Other classes
      8
      Methods of teaching Mentoring.
      Knowledge score (maximum points 100)
      Pre obligations Points Final exam Points
      Activites during lectures Test paper
      Practical lessons Oral examination 30
      Projects
      Colloquia
      Seminars 70