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:

Deutsche Telekom is operating and constantly developing and improving it’s own cloud to operate internet and telephony services (Telco applications). The Kubernetes-based cloud and the Telco applications are combined to form a TaaP - Telco as a Platform. The TaaP are thousands of servers and hundreds of applications. The energy efficiency of the TaaP is a key success criterion for optimizing costs, energy consumption, and carbon emissions. Hence, a concept of Full Stack Energy Management is established. The focus is to jointly optimize hardware, software, and services towards energy efficiency without affecting service availability and robustness. 

Problem & Challenge:

In the Telco industry, so far, HW redundancy has been the baseline for service robustness and resilience. The introduction of virtualization and containerization concepts resulted in an additional redundancy level above the hardware. Classical redundancy models no longer apply to this multi-layer Software redundancy. Moreover, so far, there is no mathematical model that captures the service availability for these new architectures. For example, a Telco-Service can be provided via 3 data centers with 50 servers each, forming 1 cluster hosting 500 Kubernetes pods. There is a mix of data center redundancy, hardware redundancy in the data centers, and Kubernetes worker node and pod redundancy.

Specific Problem Formulation:

On a TaaP there are multiple layers of redundancy in Hardware and Software. On the one hand, there are multiple site deployments, where each site has multiple hundreds of servers. On the other hand, on each site, each server has multiple redundant hardware parts, like the power supply. Moreover, a Kubernetes Cluster, which is homed on one site, hosts multiple microservices, each with a different redundancy concept like active/passive, n+1, n+m, etc. This setup of mixed HW and SW redundancy causes inefficiency and is not easy to calculate or simulate in terms of overall service availability, network, site, redundancy, and energy consumption. 

Solution Approach:

There are multiple different parameters in HW and SW that impact the service availability and energy consumption. Firstly, a comprehensive list of these parameters is required, including modeling of dependencies. Secondly, a mathematical model needs to be set up that considers all of these parameters in "one equation". Thirdly, a graphical simulation should be set up to demonstrate the dependencies and results.

Expected Outcome:

A simulation and mathematical model should be developed that considers software and hardware redundancy across multiple sites and SW layers to calculate the network-wide service availability. This is key in order to further optimize the HW and SW footprint and improve sustainability. Moreover, the model should allow the optimization of the following parameters: least required HW based on predefined service availability, least energy consumption, and best redundancy.

Working on the live network or setting up a lab deployment is out of scope of this thesis. The focus of this thesis are the modeling and simulation of the deployment.

Prerequisites

The efficient allocation of communication resources is a key challenge in future 6G in-X subnetworks, particularly in highly dynamic environments such as urban vehicular scenarios. In this work, a demonstrator for resource allocation schemes in 6G in-X subnetworks will be developed. The student will use the Simulation of Urban MObility (SUMO) framework to model realistic vehicular mobility and generate network scenarios. A backend will be implemented to integrate and execute different resource allocation algorithms, while a frontend will provide interactive visualization of network conditions and algorithm performance. The resulting demonstrator will enable the analysis and comparison of resource allocation strategies, highlighting their impact on network efficiency and service performance in dynamic vehicular environments.

Modern mobile robots often use cameras to perceive their environment or to allow remote control by a human operator. These video streams require a reliable network connection, especially when transmitted over 5G. However, network conditions in 5G can change dynamically due to movement, signal quality, or network load. This can lead to delays, reduced video quality, or even connection issues. This project focuses on making the robot “smart” in how it sends video data: instead of using fixed settings, the video stream should automatically adapt to current network conditions.

The aim is to develop an application running on a robot’s onboard computer that adjusts the quality of a stereo camera video stream based on real-time 5G network conditions. The system should balance video quality and reliability, i.e., video stability and availability, to achieve smooth and efficient transmission.

The research internship focuses on the robustness of drone communication links at the physical layer, especially with regard to jamming and spoofing attacks.

Possible tasks include:

• Literature research on PHY-layer robustness, jamming, and spoofing
• Identification of relevant attack scenarios and possible countermeasures
• Support with the implementation of countermeasures in MATLAB and Python
• Validation of the approaches using computer simulations
• Support with the implementation of SDR-based setups in GNU Radio
• Support with the setup of hardware demonstrators, integration on drone platforms, and execution of lab tests and flight experiments

Thesis Outline

The increasing use of cellular-connected drones (UAVs), especially for beyond-visual-line-of-sight applications, introduces significant challenges for airspace security, critical infrastructure protection, and regulatory compliance. Unlike traditional drones controlled via short-range radio, modern UAVs increasingly rely on 5G/6G networks for command, control, and video transmission. This shift opens up a novel opportunity: mobile network infrastructure itself can be leveraged as a large-scale sensor system for drone detection. The objective of this thesis is to investigate whether cellular-connected drones can be detected using mobile network data.

Work Items

The thesis is structured into several closely connected stages:

• comprehensive literature review that systematically surveys existing approaches to drone detection in mobile networks
• in-depth analysis of RAN and core network parameters at the MAC layer and above to identify metrics that may carry discriminative information
• generation and collection of representative data
• investigate different drone detection concepts based on the identified metrics

Depending on the type of thesis and the progress during the work, the scope may be adjusted, and not all work items need to be completed. For more details regarding the individual work items, do not hesitate to contact us.

Prerequisites