Master Thesis: A Learned World Model for Downlink Link Adaptation

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About this opportunity:
We are seeking a talented Master's student to develop an action-conditioned world model for downlink link adaptation in AI-native 5G/6G radio access networks. The thesis will combine real radio and baseband trace data, predictive modeling, and offline reinforcement learning to investigate whether synthetic model-generated trajectories can enable safer and more sample-efficient policy training.
What you will do:
• Characterize available 5G cell and baseband trace data, including radio conditions, mobility, interference, and traffic load.
• Preprocess traces into state, action, next-state, and key-performance-indicator tuples for model training and evaluation.
• Design and train a compact latent, action-conditioned world model that predicts short-horizon throughput, block error rate, channel-quality indicator, and spectral-efficiency trajectories.
• Evaluate single-step and multi-step prediction accuracy and study how well the model separates the effect of modulation-and-coding actions from external channel variation.
• Integrate the learned world model into an offline reinforcement-learning pipeline to generate synthetic rollout data.
• Compare rule-based outer-loop link adaptation, logged-data-only offline reinforcement learning, and world-model-augmented reinforcement learning.
• If time permits, investigate calibrated uncertainty estimates to restrict policy exploration to regions where predictions are reliable.
• Document methods, results, and recommendations in the thesis report and present the work at the final defense.
• Collaborate with supervisors and radio, AI, and baseband experts to ensure technical relevance and sound evaluation.
The skills you bring:
Required Skills and Qualifications
• Enrolled in or recently admitted to a Master's program in Electrical Engineering, Computer Engineering, Computer Science, Machine Learning, Wireless Communications, or a related field.
• Strong foundation in machine learning and data analysis.
• Programming experience in Python and familiarity with a deep-learning framework such as PyTorch.
• Basic understanding of wireless communications, radio access networks, or link-level performance metrics.
• Ability to work with time-series or sequential data and design reproducible experiments.
• Solid technical writing and communication skills.
• Independent, analytical, and collaborative problem-solving mindset.
Preferred Qualifications
• Experience with reinforcement learning, offline reinforcement learning, model-based reinforcement learning, or sequence modeling.
• Familiarity with latent dynamics models, recurrent state-space models, transformers, probabilistic models, or uncertainty estimation.
• Knowledge of 5G/6G link adaptation, modulation and coding schemes, channel-quality reporting, block error rate, or radio scheduling.
• Experience with MATLAB for signal-processing, trace preprocessing, or validation.
• Experience handling large experimental datasets, simulation traces, or performance-counter logs.