Computer Engineering MA, Wireless Radio Resource Management, 6 credits

Aim

The aim of this course is to provide participants with a strong theoretical foundation and applied knowledge of radio resource management (RRM) in wireless systems.

The course provides a comprehensive understanding of Radio Resource Management (RRM) in wireless communication systems, covering foundational principles, advanced techniques, and modern tools. Students will gain the ability to grasp the fundamental challenges of RRM, design and evaluate RRM strategies for diverse wireless systems, and apply optimization, game theory, and AI/ML techniques to solve resource allocation problems.

The course covers key topics such as interference management, dynamic resource allocation, power control, and mobility management while exploring the impact of emerging technologies like ultra-reliable low-latency communications (URLLC), network slicing, edge computing, and edge-cloud continuum for RRM. Through hands-on experience with simulation frameworks and modern tools, participants will learn to model, test, and assess the performance of RRM algorithms, preparing them to address real-world challenges in wireless network design and optimization.

Course objectives

Upon completion of the course the student should be able to:

• Explain the principles and challenges of RRM in wireless networks.
• Design and evaluate RRM strategies for different wireless systems.
• Apply optimization and AI/ML techniques to solve resource allocation problems (e.g., power control, channel assignment, and scheduling) to maximize network performance (e.g., spectral efficiency, energy efficiency, and fairness) in dynamic wireless environments.
• Analyze the impact of emerging services such as ultra-reliable low-latency communications (URLLC) and technologies such as network slicing, edge computing, and edge-cloud continuum för RRM.
• Evaluate distributed and cooperative RRM strategies for heterogeneous networks (HetNets) and multi-tier architectures.
• Use simulation tools to model and test RRM algorithms.
• Assess the performance of RRM strategies through quantitative analysis and simulation-based evaluation.

Content

• Fundamentals of wireless networks and RRM challenges
• Performance metrics and RRM problem formulation
• Interference modeling, coordination, and mitigation
• Link and cross-layer adaptation techniques
• Multiple access protocols and fairness-performance tradeoffs
• Handover and mobility management in cellular networks
• RRM for network slicing and edge computing.
• Resource allocation for ultra-reliable low-latency communication (URLLC)
• Heterogeneous networks (HetNets) and multi-tier architectures
• Optimization methods for RRM (optimization theory, game theory, AI/ML)
• Simulation and evaluation frameworks for RRM strategies

Entry requirements

90 credits finished courses, with at least 60 credits in Computer Engineering BA (ABC), including 12 credits programming and 6 credits computer networks.

Selection rules and procedures

The selection process is in accordance with the Higher Education Ordinance and the local order of admission.

Teaching form

The course consists of lectures, reading assignments, projects and oral presentations.

The course may also be given as a self-study course.

Examination form

I101: Study assignment, 1 Credits
Grade scale: Two-grade scale

P101: Project, 2 Credits
Grade scale: Two-grade scale

T101: Written exam, 3 Credits
Grade scale: Seven-grade scale, A-F o Fx

Link to grading criteria: https://www.miun.se/gradingcriteria.

The examiner has the right to offer alternative examination arrangements to students who have been granted the right to special support by Mid Sweden University’s disabilities adviser.

Examination restrictions

Students are entitled to three examination opportunities within one year according to the examination format given in this version of the course syllabus. After the one-year period, the examination format given in the most recent version of the course syllabus applies.

Grading system

Seven-grade scale, A-F o Fx