Mustafa Özger

Mustafa Özger

Assistant Professor, Aalborg University · Researcher, KTH Royal Institute of Technology

I am an Assistant Professor in the Department of Electronic Systems at Aalborg University, Copenhagen, and a Researcher at KTH Royal Institute of Technology, Stockholm. My research sits at the intersection of future telecommunication systems and machine learning for engineering — combining communication-theoretic reasoning with data-driven learning to design robust, uncertainty-aware methods for safety-critical and resource-constrained environments.

Portrait of Mustafa Özger
Department of Electronic Systems
Aalborg University, Copenhagen

About

My work spans wireless and networked systems, 5G/6G and non-terrestrial networks, AI-native network management, mobility management, localisation, uncertainty-aware inference, and system-level optimisation. I am interested in when learning-based methods can be made robust, data-efficient, and trustworthy enough to operate inside real communication infrastructure, and in how information-theoretic principles can make those methods more reliable under data scarcity and distributional shift.

I have experience in industrially motivated projects and multi-partner research coordination, with applications spanning communications, defence-relevant autonomous systems, and cyber-physical infrastructures. I teach and supervise across BSc, MSc, PhD, and postdoctoral levels.

Research interests · machine learning for engineering systems · information-theoretic learning · wireless communications · 5G/6G and non-terrestrial networks · AI-native network management · URLLC and dependability · mobility management · localisation and sensing · system-level optimisation

Research themes

Machine learning for engineering systems

Reinforcement learning, multi-agent and hierarchical RL, data-driven control, uncertainty-aware modelling, explainability, and domain-grounded learning for complex engineering systems.

Information-theoretic learning and uncertainty

Information-theoretic measures for model selection and dataset design, channel and signal classification, representation learning for physical-layer data, and reliable inference under data scarcity and distributional shift.

Communication systems and networked engineering

Wireless networks, non-terrestrial networks, resource allocation, mobility management, URLLC, localisation, sensing, and signal processing.

System-level networking and management

Reliability, traffic management, network architecture, and AI-assisted resource allocation and control across terrestrial, aerial, and non-terrestrial systems.

Industrial and collaborative research

Project leadership and collaboration with Airbus, Ericsson, Deutsche Telekom, Turkcell, Viavi, and other stakeholders through national and international consortia.

Selected contributions

News

Selected publications

  1. Independent PPO-based robust and scalable three-dimensional configuration for green massive MIMO networks Q. Wang, S. Zhang, M. Özger, and C. Cavdar. IEEE Transactions on Green Communications and Networking, Early Access, 2026.
  2. Hierarchical multi-agent DRL-based dynamic cluster reconfiguration for UAV mobility management I. A. Meer, K.-L. Besser, M. Özger, D. Schupke, H. V. Poor, and C. Cavdar. IEEE Transactions on Cognitive Communications and Networking, vol. 12, pp. 4957–4971, 2025.
  3. 6G for connected sky: holistic adaptive combined airspace and non-terrestrial network architecture S. Zhang, M. Özger, S. S. S. G. Seeram, I. Godor, L. Feltrin, A. Nordlow, J. Pfeifle, L. Toka, G. Biczok, D. A. Schupke, and C. Cavdar. IEEE Wireless Communications, vol. 32, no. 5, pp. 204–211, 2025.
  4. Mobility management for cellular-connected UAVs: model-based versus learning-based approaches for service availability I. A. Meer, M. Özger, D. Schupke, and C. Cavdar. IEEE Transactions on Network and Service Management, vol. 21, no. 2, pp. 2125–2139, 2024.
  5. A survey of wireless networks for future aerial communications (FACOM) A. Baltaci, E. Dinc, M. Özger, A. Alabbasi, C. Cavdar, and D. Schupke. IEEE Communications Surveys & Tutorials, vol. 23, no. 4, pp. 2833–2884, 2021.
  6. Risk-aware resource allocation for URLLC: challenges and strategies with machine learning A. Azari, M. Özger, and C. Cavdar. IEEE Communications Magazine, vol. 57, no. 3, pp. 42–48, Mar. 2019.

See the full publication list →

Experience

Academic appointments
Funded projects & industrial engagement
Education

Opportunities

Prospective students

I welcome inquiries from highly motivated BSc, MSc, and PhD students interested in wireless communications, non-terrestrial networks, machine learning for engineering systems, reinforcement learning, localisation, and network management. Please include a short motivation, your CV and current academic status, and — if available — transcripts, a project summary, or links to code or papers.

Prospective postdocs

I am also interested in hearing from postdoctoral candidates whose background aligns with 5G/6G systems, non-terrestrial networks, AI-native networking, learning-based control, communication theory, or reliable inference for engineering applications. A concise research statement, a note on how your expertise fits ongoing or future projects, and relevant publications or preprints are appreciated.

Contact

Mustafa Özger

Department of Electronic Systems
Aalborg University, Copenhagen, Denmark

mozger@es.aau.dk

Google Scholar

ORCID

AAU profile

KTH profile