Time Sensitive Networking (TSN) provides a variety of different mechanisms providing real-time communication. Especially, TSN is able to transmit periodic time-triggered traffic with strict real-time requirements.

This thesis focuses on the time-aware shaper (TAS) standardized in IEEE 802.1Qbv and the per-stream filtering and policing (PSFP) mechanism defined in IEEE 802.1Qci. The goal is to develop an algorithm combining a TAS and a PSFP schedule. The TAS schedule should be secured by a time-based PSFP schedule. The PSFP schedule should drop frames or reduce their priority which are not matching the expected arrival interval.
The algorithm has to be evaluated in a testbed considering traffic scenarios with multiple time-triggered streams.

The 3GPP standard has established a maximum time synchronization error, the indoor 5G network may be able to handle a higher value proposed by an earlier simulation. To validate and determine such a result, it is necessary to use real hardware to create a small network and test related aspects of it. Upper error limits serve a useful purpose in many network applications. The developer is able to establish the maximum permissible error by the network requirements to aid in the construction of networks.

Providing Quality of Service (QoS) to emerging time-sensitive applications such as factory automation, telesurgery, and VR/AR applications is a challenging task [1]. Time Sensitive Networks (TSN) [2] and Deterministic Networks [3] were developed for such applications to guarantee ultra low latency, bounded latency and jitter, and zero congestion loss. The objective of this work is to develop a methodology to guarantee bounded End-to-End (E2E) latency and jitter in large-scale networks.

C++, Expeience with OMNET++, KNowledge of TSN.

Time-Sensitive Networking (TSN)is a set of standards[1]developedby IEEE 802.1 Task Groupto enableEthernet networks to giveQuality of Service (QoS)guarantees for time-sensitiveor mission-critical traffic and applications.VariousTSN standards provide differing QoSguaranteesand require different functions to be implemented in hardware. As devices
frommultiplevendorsneedtooffermutuallycompatiblefunctions,profilessuchas IEEE60802forIndustrialAutomation[2]arebeingdefined.These profilesfocusona commonset of functions and configurations in order to decreasethe complexity which possible variations in standards might create.

REST API, Knowledge and Experience with 5G systems, Undesrstanding of TSN.

Python.

This research internship aims to assess the impact of time synchronization discrepancies between base stations located in different cells. When time offsets occur, transmissions from one cell may reach user equipment (UEs) in neighboring cells at unintended times, resulting in interference and potential performance degradation.

This research internship will set up a MATLAB simulation to evaluate this scenario.

1. Understanding of 5G systems.
2. Design of Experiments.
3. Experience with MATLAB.

The research will utilize the 5G-NR functions available in Matlab for a two-base station scenario, each having multiple UEs connected in both cells. The throughput and block error rate of a UE at the edge of the cell will be evaluated for multiple simulations for different values of delayed transmission in the interfering base station. The study will also incorporate the other BS parameters, such as BS transmit power and the distance between the two BSs, and analyze their influence on the results obtained.

The results expected from this work are the enhancement of the 5G/6G tested setup with additional features on the Radio Access Network (RAN) and Core Network (CN). The work is focused on the OpenAirInterface (OAI) [1] platform, which forms the basis of the testbed setup. The expected outcome is to have improvements in wireless resource scheduling, focused on the uplink (UL), power management, and core network function management. 

[1] N.Nikaein, M.K. Marina, S. Manickam, A.Dawson, R. Knopp and C.Bonnet, “OpenAirInterface: A flexible platform for 5G research,” ACM SIGCOMM Computer Communication Review, vol. 44, no. 5, 2014.

• Good C/C++ experience
• Good Python knowledge
• RAN and CN architecture understanding is a plus