Research Project || Thesis||  Monthly compensation opportunity for promising candidatesWe are excited to announce an exceptional thesis opportunity for students and researchers interested in the cutting-edge field of networking and communication technologies. This thesis will delve into the integration and enhancement of Time-Sensitive Networking (TSN) standards with 5G Systems (5GS), focusing specifically on the IEEE 802.1Qbv standard, also known as Time Aware Shaper, and the challenges of indeterminism in 5G networks.  
Background and Context
Time-Sensitive Networking (TSN) represents a crucial set of standards developed by the IEEE 802.1 Task Group. These standards enable Ethernet networks to provide Quality of Service (QoS) guarantees for time-sensitive or mission-critical traffic and applications. As TSN comprises various standards each offering different QoS guarantees and requiring distinct hardware functions, its integration with 5G Systems, which are inherently complex and enhanced for Ultra-Reliable Low-Latency Communication (URLLC) in Releases R15, R16, and R17, presents unique challenges and opportunities.The interoperability of TSN and 5GS aims to achieve minimal impact on TSN entities while ensuring high-level connectivity options and user plane functionality.
Research Focus and Objectives

The core of this thesis/research project  is to develop and extend an optimization scheduling model for TSN-5G networks. This task involves addressing the inherent indeterminism of 5G networks, a significant challenge when creating scheduling algorithms. The concept of gamma robust scheduling, a technique in mathematical optimization, will be central to this research.The candidate will work on extending an existing optimization scheduling model, originally designed for TSN, to suit the TSN-5G network context. This extension must consider the non-deterministic delay factors intrinsic to 5G networks.

Preferred Candidate :  

• Took the elective course 'Optimization for Non-Mathematicians' 

• Took the course 'Mobile Networks'
• knows Python 
• Knows or is interested in Deep Learning / Reinforcement Learning
• Used Omnet++

Any highly motivated student who does not possess all the skills but is willing to delve deeper and be interested in research can apply.  Apply with your CV and Leistungsübersicht 
directly as an email to the address syed-tasnimul.islam@etit.tu-chemnitz.de. Subject: Application for TSN-5G Thesis/Research project. 

Professur Kommunikationsnetze | Institut für Informationstechnik | Fakultät für Elektrotechnik und Informationstechnik (Syed Tasnimul Islam)

Einsatzort:
09126 Chemnitz
Deutschland

Telefon: 0371 / 531 34820
Fax: 0371 / 531
E-Mail: syed-tasnimul.islam@etit.tu-chemnitz.de