Master Thesis: Energy-Aware Traffic Control in 6G

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I korthet

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.

Ansvarsområden

  • 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.

Krav

  • 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

Önskade kvalifikationer

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

Färdigheter

PythonC++RustMachine LearningReinforcement LearningRRCL3
#6G#Energy Efficiency#Traffic Control#Network Optimization#RRC#Machine Learning#Reinforcement Learning
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Företag

Ericsson

Publicerade jobb

för 5 dagar sedan

Anställningstyp

Praktik

Arbetsform

På plats

Erfarenhetsnivå

Student

Platser

Linköping, Sweden

Kvalifikation

Masterexamen

Sökande

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