Data-Driven Radio Planning and Cellular Network Optimization (2024)
Abstract / truncated to 115 words
Abstract Integrating AI into wireless network design and management is essential for creating self-sustaining 6G networks. A key challenge is the development of automated network procedures with minimal human intervention, leveraging real-time monitoring data for immediate feedback. These advancements promote data-driven decision-making but pose risks related to data availability, safety, and the black-box nature of learning algorithms. This cumulative thesis proposes and evaluates novel procedures and algorithms for data- driven radio planning and cellular network optimization, addressing practical challenges in applying learning-based methods on real-world deployments. It emphasizes the utility of monitoring data and the integration of model-based and model-free methods, ensuring the scalability and safety of adaptive network procedures across diverse environments. The first ...
data planing – localisation – machine learning – 5G
Information
- Author
- Lukas Eller
- Institution
- TU Wien
- Supervisors
- Publication Year
- 2024
- Upload Date
- May 29, 2025
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