Master Thesis Optimizing Multi-Flow Congestion Control for COTS 5G Devices via Machine Learning

Master Thesis Optimizing Multi-Flow Congestion Control for COTS 5G Devices via Machine Learning

Robert-Bosch-Campus 1, 71272 Renningen, Germany

Full-time

Robert Bosch GmbH

Company Description

At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid : we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference.

The Robert Bosch GmbH is looking forward to your application!

Job Description

In today's advanced 5G networks, multiple applications share the same communication link and often compete for limited resources. For connected cars, such uncoordinated competition can render critical car-to-cloud applications (e.g., teleoperated driving) unreliable.

• In this master thesis you will study the state-of-the-art congestion control mechanisms for time-critical applications.
• Additionally, you will identify the metrics to monitor and optimize with QoS (Quality of Service)-aware connectivity control on multi-application devices using COTS (Commercial Off-the-Shelf) 5G equipment.
• Furthermore, you will also design and implement an intelligent connectivity control mechanism using machine learning techniques to maximize network efficiency while ensuring consistent QoS across diverse traffic types.
• Last but not least, you will compare the developed approach against a selected baseline to analyze improvements in efficiency and performance.

Qualifications

Additional Information

Start : according to prior agreement

Duration : 6 months

Requirement for this thesis is the enrollment at university. Please attach your CV, transcript of records, examination regulations, a link to your Git repository and if indicated a valid work and residence permit.

Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity.

Need further information about the job?

Min Hee Jo (Functional Department)

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