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IK2523 Intelligent Wireless Networking 7.5 credits

Information per course offering

Course offerings are missing for current or upcoming semesters.

Course syllabus as PDF

Please note: all information from the Course syllabus is available on this page in an accessible format.

Course syllabus IK2523 (Autumn 2027–)
Headings with content from the Course syllabus IK2523 (Autumn 2027–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

  • Wireless Network Fundamentals and Performance
  • Medium Access Control
  • Network Resource Scheduling
  • Interference Management and Power Control
  • User Association, Handover, and Load Balancing
  • Energy-Efficient Wireless Networks
  • Heterogeneous and Integrated Wireless Networks
  • Programmable and Disaggregated Wireless Networks
  • AI for Wireless Network Management

Intended learning outcomes

After passing the course, the student should be able to

  • explain the fundamental principles, architectures, protocols, and resource-management mechanisms of modern wireless networks;
  • explain medium-access, scheduling, interference-management, power-control, association, handover, and load-balancing mechanisms;
  • describe emerging architectures and enabling technologies for intelligent wireless networking, including heterogeneous networks, Open RAN, network virtualization, edge computing, and AI-based network management;
  • formulate and apply mathematical models for analysing wireless-network performance in terms of capacity, throughput, delay, reliability, fairness, interference, and energy consumption;
  • analyse and design resource-allocation, scheduling, power-control, interference-management, association, and handover mechanisms;
  • implement and use simulation models to evaluate wireless-network protocols and resource-management algorithms;
  • evaluate network-management solutions under different traffic, mobility, interference, and service requirements;
  • analyse trade-offs among spectrum utilization, energy consumption, infrastructure resources, quality of service, and network performance;
  • critically evaluate the applicability and limitations of conventional optimization and AI-based approaches to wireless-network management;
  • interpret and discuss contemporary developments and scientific literature in intelligent wireless networking.

Literature and preparations

Specific prerequisites

Knowledge of basic communication systems corresponding to completed course EP2130/EP2120 or knowledge of computer networks corresponding to completed course IK2218/IK1203/EP1100.

Knowledge of wireless communications corresponding to completed course in IK2522. Active participation in IK2522 during study period 3 of the same academic year is considered equivalent to completion of the course.
Registered students are expected and considered to be actively participating.

Literature

You can find information about course literature either in the course memo for the course offering or in the course room in Canvas.

Examination and completion

Grading scale

A, B, C, D, E, FX, F

Examination

  • TEN1 - On-Campus Digital Exam, 4.0 credits, grading scale: A, B, C, D, E, FX, F
  • PRO1 - Project, 3.5 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability. The examiner may apply another examination format when re-examining individual students. If the course is discontinued, students may request to be examined during the following two academic years.

Examiner

No information inserted

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Main field of study

Computer Science and Engineering, Electrical Engineering

Education cycle

Second cycle