Master Thesis: Energy-Aware Traffic Control in 6G

Technology, Data & Digital · Software & Web Development · Software Engineering

In short

Master's thesis project focusing on energy-aware traffic control in 6G networks, aiming to reduce energy consumption through smarter L3 control while ensuring QoS. The project involves system-level simulation, implementing baseline policies, and designing energy-aware policies, including reinforcement learning.

Responsibilities

Reduce network energy consumption through smarter L3 control while respecting QoS guarantees.
Quantify the energy–performance trade-off.
Model L3 decisions (RRC states, cell activation/sleep, admission control) and their mapping to network energy consumption using a system-level simulator.
Implement a rule-based baseline aligned with 3GPP standards.
Design energy-aware policies, including a reinforcement-learning policy that coordinates cell sleep with RRC.
Evaluate across load levels and produce energy–performance trade-off curves.
Conclude with recommendations for energy-aware 6G L3 design.
Present results.

Requirements

Master´s student in electrical engineering, computer science, or similar
Programming experience in Python (and/or C++, Rust)
Basic machine learning and/or reinforcement learning knowledge
Interest in 3GPP protocol layers (RRC/L3)
Strong teamwork skills

Desired Qualifications

Ideal for two students with a split focus: one leaning toward protocols/systems, one leaning toward ML.

Skills

PythonC++RustMachine LearningReinforcement LearningRRCL3

#6G#Energy Efficiency#Traffic Control#Network Optimization#RRC#Machine Learning#Reinforcement Learning

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