Thesis (Master) - Sidelink-based radio resource allocation for Cooperative Robotics

Siemens AG; Garching an der Alz; Sur place; EUR 20 000 - 27 000; Temps partiel

Avantages offerts par ce poste

Research opportunities

Mentorship

Lab access

Career growth

Résumé du poste

Siemens AG in Bayern bei Garching sucht eine engagierte Fachkraft für Wireless Communications Forschung. Sie charakterisieren Inkompatibilitäten von 3GPP Rel.16 Mode 2 und URLLC, entwickeln schnelle Kanalbeobachtungsmechanismen und untersuchen neuromorphe Ansätze wie SNNs für MAC-Resource-Selection.

Sie vergleichen Mechanismen mit der Baseline, bewerten Latenz und Zuverlässigkeit in TS 22.104 und prüfen Skalierbarkeit in Uni- und Gruppen-Sidelink-Szenarien in einer Fabrikumgebung.

Qualifications

• Im Studium immatrikuliert in Elektrotechnik, Informatik oder verwandtem Bereich.
• Fortgeschrittene Programmierkenntnisse in Python und C/C++.
• Kenntnisse zu LTE/5G/NR-V2X von Vorteil.

Responsabilités

• Analytische Charakterisierung der Inkompatibilität von Rel.16 Mode 2 mit URLLC-Anforderungen.
• Untersuchung leichter, sofortiger Kanalbeobachtungsmechanismen für MAC-Schicht.
• Benchmarking gegen Rel.16-Baseline im Kontext industrieller Sidelink-Szenarien.

Connaissances

Python

C/C++

Wireless systems

Open Air Interface

NS3 5G-LENA

Keras

TensorFlow

PyTorch

Formation

Electrical Engineering

Computer Science

Outils

NS3 5G-LENA

Description du poste

Aufgaben:

You begin by analytically characterizing the incompatibility between the standard 3GPP Rel.16 Mode 2 autonomous resource selection procedure and sub-millisecond URLLC requirements in cooperative robotic Sidelink scenarios, using modeling and/or simulation in NS-3Building on this, you investigate lightweight and instantaneous channel observation mechanisms as alternatives to the standard multi-millisecond sensing window, evaluating their feasibility and accuracy in dynamic factory channel conditionsOne possible path could be to investigate neuromorphic computing approaches - in particular event-driven neural network architectures such as Spiking Neural Networks (SNNs) - as a candidate inference engine for MAC-layer resource selection, with focus on inference latency feasibility within the available PDB budget of 1-3 msFinally, you benchmark the proposed mechanisms against the standard 3GPP Rel.16 Mode 2 baseline in terms of resource selection latency and reliability under TS 22.104, and assess their scalability for cooperative robot pairs (unicast) and small groups (groupcast) within a factory deployment area

Qualifikation:

Education : You are currently enrolled in a degree program in Electrical Engineering, Computer Science, or a related field with a focus on wireless communicationsExperience and Skills :You possess advanced programming skills in Python and C/C++You have knowledge of the structure and operation of wireless communication systems such as LTE, 5G, or NR-V2XExperience with radio resource allocation algorithms and tools such as Open Air Interface, NS3 5G-LENA, Keras, Tensorflow, or Pytorch is beneficialWays of Working : You work independently, analytically, and demonstrate a high level of commitment and initiativeLanguages : You have very good English skills

Wir bieten:

Exciting research and development projects that put your theoretical knowledge into practiceIndividual supervision and support from experienced experts in your fieldAccess to the latest technologies, laboratories, and resourcesDiverse opportunities to contribute your ideas and actively shape the projectsExcellent career opportunities through contact with potential employers

Über Uns:

At Siemens, we believe that feeling valued and included is the foundation for doing great work. That's why we aim to create an inclusive workplace where everyone feels a sense of belonging, and where individual perspectives and experiences are celebrated. Our commitment to fairness and respect extends to every applicant.The world never stands still. And new challenges arise every day. With a passion for questioning things, for supplying ideas, and intelligently driving things forward we are helping society move towards a smarter tomorrow. Be it with technologies that reduce carbon emissions in cities or hyperintelligent robots. This is how we are able, to tackle the most important projects and push them forward together. Help us shape the future.