Motivation

Emerging 5G applications, particularly in Ultra-Reliable Low-Latency Communications (URLLC), require strict guarantees on latency and reliability. Hybrid Automatic Repeat Request (HARQ) is a key mechanism for improving transmission reliability, but retransmissions, decoding delays, and feedback latencies introduce variability in packet delays. While analytical models provide insights into the resulting Delay Violation Probability (DVP), their accuracy may be limited under practical deployment conditions. Therefore, data-driven approaches based on real measurements are needed to improve delay prediction and support latency-aware network optimization.

Problem Statement

Existing delay analysis frameworks for 5G HARQ primarily rely on analytical modeling assumptions and are often validated only through simulations. Furthermore, current machine learning models for DVP prediction do not fully exploit real-world testbed data and typically lack support for downlink-specific HARQ characteristics. As a result, there is a gap between theoretical delay analysis and practical delay prediction in operational 5G systems.

Thesis Goal

The goal of this thesis is to collect HARQ-related delay measurements from a 5G testbed and use them to validate and extend an existing machine learning model for predicting Delay Violation Probability. The work will investigate the relationship between HARQ retransmissions, decoding latency, feedback latency, and overall packet delay. In addition, the existing prediction model will be extended to incorporate downlink HARQ features, and its prediction accuracy will be evaluated using experimentally collected data.

Due to the increasing demands of Industry 4.0, 5G networks are increasingly used to support mobility in Time-Sensitive Networking (TSN) applications such as remote robot control and IoT systems. These applications must support a wide variety of traffic types, including best-effort, rate-sensitive, and low-latency traffic streams.

Recent research [1] proposes joint scheduling between 5G and TSN, focusing on an aligned resource allocation of the 5G RAN scheduler and TSN mechanisms Time-Aware Shaper (TAS) and Per-Stream Filtering and Policing (PSFP). In particular, deterministic scheduling in the uplink is sensitive to jitter because of the grant-based control signaling mechanism in 5G. To address this challenge, [1] proposes a heterogeneous uplink scheduler that combines grant-based and grant-free transmissions. This approach enables static allocation of radio resources for time-sensitive traffic streams while still allowing dynamic allocation for traffic with non-deterministic sending behavior.

However, the approach has so far been evaluated only through simulations, while experimental validation in real testbed environments remains an open challenge. The goal of this thesis is therefore to experimentally evaluate the TSN-5G scheduling framework in an end-to-end testbed. The work involves the following tasks:

• Implement heterogenous 5G RAN Scheduler in OpenAirInterface RAN and as xApp
• Configure TSN Switches (TAS, PSFP) considering the 5G uplink bridge delay
• Experimental evaluation of the scheduling framework under different scenarios, including
• different TDD patterns
• different traffic scenarios

[1] L. Becker, Y. Deshpande, and W. Kellerer, “Joint Resource Allocation to Transparently Integrate 5G TDD Uplink with Time-Aware TSN,” arXiv preprint arXiv:2511.23373, 2025.

Voraussetzungen

Motivation
Autonomous drone swarms represent a key research topic in modern robotics and communication systems. They are increasingly applied in safety-critical domains such as disaster response, industrial inspection, and military reconnaissance. A fundamental prerequisite for the reliable operation of such systems is a robust, real-time capable, and secure communication between the individual drones.

In practice, many existing systems rely on centralized control or coordination entities, which introduce single points of failure. Furthermore, distributed swarms often assume an implicit trust model, where received messages are processed without cryptographically secured authorization or consistency mechanisms. This makes the overall system vulnerable to node failures, communication faults, or targeted attacks.

The Data Distribution Service (DDS) has established itself as a powerful standard for decentralized, real-time publish/subscribe communication and is widely used in the context of ROS 2. However, DDS primarily addresses latency, reliability, and quality-of-service requirements, while it does not provide mechanisms for trust establishment, identity management, or consistent decision-making in distributed systems.

Blockchain technology offers decentralized consensus mechanisms, immutable state records, and cryptographic identities, providing promising solutions to these challenges. Due to its limited real-time capabilities, however, blockchain technology is not suitable for the direct exchange of time-critical sensor data.

The combination of DDS and blockchain technology therefore enables the realization of a hybrid communication model that combines real-time data exchange with decentralized trust and fault tolerance.

Objective of the Thesis
The objective of this master’s thesis is the development and evaluation of a decentralized and trusted communication architecture for autonomous drone swarms that:
• avoids centralized control entities,
• enables real-time data transmission,
• and provides cryptographic protection for safety-critical decisions.

This leads to the following research question:
How can a hybrid communication architecture based on DDS and blockchain technology be designed to meet the real-time requirements of autonomous drone swarms while providing a decentralized trust and authorization structure?

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.

Voraussetzungen

The Wireless Sensor Networks lab offers the opportunity to develop software solutions for the wireless sensor networking system, targeting innovative applications. For the next semester, a position is available to assist the participants in learning the programming environment and during the project development phase. The lab is planned to be held on-site every Tuesday 15:00 to 17:00.

Voraussetzungen