Motivation
The development of modern 5G User Equipment (UE) requires flexible, reproducible test environments. In particular, the radio communication tester CMX500 from Rohde & Schwarz enables reproducible, standard-compliant testing of 5G UEs under controlled conditions by emulating a 5G base station.

To enable early testing without a hardware setup, a software-based virtual CMX500 environment is being developed. This allows software-based UEs to be tested and enables validating protocol implementations without requiring UE hardware or a complete UE protocol stack.

The Open Radio Access Network (O-RAN) architecture provides a suitable framework for layered testing, as it separates radio access network functions via standardized interfaces, commonly known as O-RAN functional splits. These splits define where functionality is divided between different RAN components and, in the context of testing, allow individual components or protocol layers to be validated at defined interface points.

Problem
A software-based UE-side counterpart can further support the development of the virtual CMX500. The OpenAirInterface (OAI) RF Simulator (RFSIM) provides a promising basis, as it already supports standard-compliant UE simulation and replaces the RF device with an interface that forwards raw IQ samples over a TCP connection. This IQ-sample-based connection is located at the same functional level as O-RAN split 8, which defines the interface between the lower physical layer and the RF device. However, the current RFSIM mechanism is specific to the OAI simulation environment and is therefore not compatible with the O-RAN specification and cannot directly communicate with the virtual CMX500.

Goal of the thesis
The goal of this thesis is to investigate how OAI RFSIM can be extended to act as a UE test device connected to the virtual CMX500 emulator. The main task is to adapt the existing RFSIM IQ sample exchange mechanism to meet the CMX500 interface requirements, following the O-RAN split 8
approach.

This includes implementing Enhanced Common Public Radio Interface (eCPRI), a packet-based fronthaul transport protocol commonly used to carry time-sensitive radio data between baseband processing and radio units. In this thesis, eCPRI is used to encapsulate the raw IQ samples from OAI RFSIM and transport them over the corresponding fronthaul interface towards the virtual CMX500 environment.

Other O-RAN functional splits may be discussed conceptually or considered for future extensions.

Pre-conditions

• Experience in C programming
• Basic knowledge of the 5G protocol stack and the O-RAN concepts.
• Basic understanding of network programming, especially socket-based communication

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.

5G networks increasingly serve mission-critical applications such as industrial automation, autonomous vehicles, and remote control. These use cases require highly deterministic communication with strict bounds on latency, jitter, and reliability. Time-Sensitive Networking (TSN), originally designed for wired industrial Ethernet, provides mechanisms for deterministic communication and is now being integrated with 5G to provide mobility. 

Although the reliability mechanism, Frame Replication and Elimination for Reliability (FRER), is supported in TSN-5G systems, the configuration is typically fixed or manually configured. Real 5G environments, however, exhibit dynamic and sometimes unpredictable wireless channel conditions. This necessitates a Central Network Controller that dynamically reconfigures the reliability mechanisms. 

This thesis aims to simulate and analyze the dynamic management of FRER and evaluate their performance under varying channel conditions.

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.

Prerequisites

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?

Motivation
Time-Sensitive Networking (TSN) enables deterministic communication in Ethernet networks by providing mechanisms for bounded latency, low jitter, and traffic isolation. One important TSN mechanism is the Time-Aware Shaper (TAS), defined in IEEE 802.1Qbv, which controls the transmission of different traffic classes by opening and closing queue gates according to a cyclic schedule. This allows time-critical traffic to be transmitted in reserved, isolated time slots.

In industrial communication systems, 5G is increasingly considered as a flexible wireless extension of TSN. However, unlike wired TSN links, a 5G link introduces variable delay due to wireless channel conditions, scheduling, and retransmissions. This delay variation, or jitter, can disturb deterministic end-to-end communication.

A promising approach is to use TAS scheduling in the wired TSN domain to compensate for the measured delay behavior of the 5G link. To do this effectively, the 5G link must first be characterized experimentally. Based on measured delay and jitter distributions, suitable TAS schedule parameters can be derived, such as gate opening times, guard intervals, cycle times, and offsets. 

Problem Description

TAS schedules are typically designed for deterministic wired networks where link delays are relatively stable and predictable. In a 5G-TSN scenario, however, the 5G segment behaves as a stochastic link with variable latency. If the TAS schedule is configured without considering this delay variation, packets may arrive too early or too late for the intended transmission window. This can lead to additional queuing delay, missed transmission slots, increased jitter, or loss of deterministic behavior.

The key problem is therefore to determine how to configure TAS parameters when a 5G link is part of the end-to-end communication path. This requires understanding the delay behavior of the 5G link under different operating conditions and deriving practical rules for TAS schedule configuration from this characterization.

The internship will focus on a testbed-based investigation. The 5G link will be measured experimentally, the resulting delay and jitter behavior will be analyzed statistically, and initial TAS scheduling guidelines will be derived. If time permits, the work may also formulate the TAS configuration as an optimization problem and evaluate optimized schedules.

Goal

The main goal of this internship is to characterize the delay behavior of a 5G link in a TSN-related testbed and to derive practical guidelines for configuring TAS schedules to reduce end-to-end jitter.

The minimum expected outcome is:

1. A completed measurement campaign of the 5G link delay and jitter behavior.

2. A statistical characterization of the measured 5G delay distribution.

3. A first set of TAS configuration guidelines based on the measured delay behavior.

4. If feasible, an initial testbed evaluation of the proposed TAS settings.

An optional extended goal is to formulate the TAS schedule configuration as an optimization problem, for example, minimizing jitter or end-to-end latency while respecting measured delay bounds and TAS cycle constraints.

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.

Prerequisites