Student Work

Robustness Surfaces are visual tools to understand the resilience of networks in the event of failures. This thesis aims to develop a tool to generate robustness surfaces to quantitatively measure the robustness of networks.

References:

[1] Rueda, Diego F., Eusebi Calle, and Jose L. Marzo. "Robustness comparison of 15 real telecommunication networks: Structural and centrality measurements." Journal of Network and Systems Management 25 (2017): 269-289.

[2]Manzano, Marc, et al. "Robustness surfaces of complex networks." Scientific reports 4.1 (2014): 6133.

Prerequisites

The increasing demand for Natural Language Processing (NLP) tasks has led to the development of Large Language Models (LLMs). However, their high memory requirements have limited their deployment on Edge and IoT devices. To address this challenge, multiple distributed LLM frameworks have emerged.

This project aims to benchmark and compare the performance of existing frameworks on Edge and IoT devices. We will evaluate their ability to optimize resource utilization, minimize latency, and provide cost-effective execution of LLMs on devices with limited compute resources.

The goal is to identify the strengths and weaknesses of each framework, including their scalability, reliability, and fault tolerance in the face of network disruptions and device failures. 

A reliability block diagram is a tool used to measure the availability of a system (a network, in our case). However, the existing tools as software packages do not work with bidirectional links. 

This work aims to build a tool that can evaluate the availability of a network based on the RBD. The back end should be in C++. This back end should be wrapped in a Python function.

Prerequisites

Molecular communication (MC) is a novel communication paradigm envisioned to enable revolutionary future medical and biological use cases such as in-body networks for the diagnosis and treatment of diseases. MC is based on the transport of molecules for information exchange and represents a very energy-efficient and bio-compatible communication mechanism on the centimeter to nanometer scale. The communication nodes can be very small as they will be based on artificial cells or other types of tiny nano-machines.

In order to realize complex applications, such as targeted drug delivery or the detection and localization of infections and tumors, nano-machines must cooperate and communicate. The specific properties and mechanisms in biological environments and at very small scales lead to several challenges:

• Novel channel models and conditions based on diffusion and flow of molecules.
• Extremely slow speeds compared to electromagnetic waves.
• Highly stochastic behavior of the molecules.
• Low capability of future nano-machines, not able to conduct complex computations or sophisticated algorithms.

Therefore, research on MC networks is crucial to enable a future internet of bio-nano things (IoBNT) integrating classical and molecular networks.

In this thesis, the student will work on the topic of chemical reaction networks (CRNs), which represent a possible substrate for computations and programmability in biological systems. A CRN is built from a number of coupled chemical reactions and is designed to turn a certain concentration of input molecules into a concentration of output molecules.

The student will be tasked with implementing a CRN that approximates a real signal processing algorithm, namely successive interference cancellation (SIC). SIc could be used, for example, to realize non-orthogonal multiple access schemes in a larger MC network. 

The CRN will be designed conceptually and implemented using Python. Then, the CRN will be evaluated rigorously using both deterministic solvers based on differential equations, as well as stochastic simulations that take into account individual random molecule interactions.

Semantic communication enables communication of discrete symbols. Even when a proportion of symbols are lost, an appropriate synthesis of the intended meaning is possible.

Error concealment attempts to minimize the impact of errors (symbol loss), by approximating lost symbols.

This thesis applies machine-learning to the hypothesis that:
If semantic symbols are lost, given first order motion model video conferencing, then, with the aid of mutual information, temporal information, and the structure of semantic symbols, machine learning can be used to create an error concealment technique more performant than an algorithmic technique, because the patterns in the data are more complex than easily captureable algorithmically.

This thesis will focus on the next generation air to ground communication standard: LDACS. It specifically discusses the IP Layer of the LDACS and mobility mechanisms behind it which enable a seamless handover for the aircraft.

Implement a particle-based simulation framework for molecular communication networks:

Use e.g. Python or MATLAB

Main target scenario: cylindrical tube with multiple transmitters and receivers

Effects: Diffusion, laminar/uniform flow -> output: particle trajectories

Enable different types of transmitters and receivers (e.g. point, cross-sectional distribution, volume) Flexibility in placement of TX and RX and shape of the initial molecules distribution  

Implement and evaluate a non-orthogonal multiple access scheme based on the distance and the emitted number of molecules from each TX

Main-stream serving paradigms for distributed models, such as data parallelism and model parallelism, are not suitable when it comes to inference for tasks that require low latency and have atomic input streams. A recent effort, Sensai, proposes a new generic approach called class parallelism that aims to distribute a base convolution neural network (CNN) model across several homogeneous machines.

    The model distribution paradigm decomposes a CNN into disconnected subnets, each responsible for predicting specific classes or groups of classes. They claim that this approach enables fast, in-parallel inference on live data with minimal communication overhead, significantly reducing inference latency on single data items without compromising accuracy.

    Class Parallelism, however, comes with its own set of challenges and limitations. For instance, since the generated models should be created in a homogeneous manner; they share similar characteristics. Further, regardless of the input, all sub-models have to be executed to get the final prediction, which directly impacts the robustness and scalability of the system.

    During the first stage of the thesis, our goal is to reproduce the results from the paper. Later, we want to improve the existing method to become more robust and possibly extend it to new use cases besides image classification. Finally, if time permits, we want to evaluate the trained models in an edge environment.

This master’s thesis addresses the challenge of classifying encrypted traffic in large-scale networks. Traditionally, network operators could monitor and deeply analyze the traffic flowing through their infrastructure to optimize traffic management. However, since the rise of encrypted communications and security concerns, the details accessible by network operators have shrunk to a very high degree. Thus, the current project aims to propose and implement a method for in-network, flow-based encrypted traffic classification.

Alongside the increasing difficulty in classifying and analyzing encrypted traffic comes also the limitations of in-network classification in traditional networks. Traditional network monitoring and analysis methods suffer from little scalability. Therefore, the question addressed in this thesis is whether software-defined networks (SDN) are suitable for in-network traffic classification, relying on SDN-enabled switches distributed across the network operator’s infrastructure. For this classification task, a decision tree classifier implemented using the P4 language and running on the switches is proposed. Finally, the performance and accuracy of implementing such processing will be assessed.

Key points:

• Large-scale traffic classification

• ETC is a hot topic. It provides information to the network operator for optimizing traffic management

• SDN is the new networking paradigm that helps by relying on switches across the network for classification instead of relying on a central network element for it, which could be a bottleneck and could hinder getting real-time analytics.

• Decision Trees as the Machine Learning model used for traffic classification, based on previous work done in the field.

Prerequisites

Optimal Placement of MAF chains considering Priority

The increasing complexity of video analysis tasks has led to the development of sophisticated machine learning applications that rely on multiple interconnected deep-learning models. However, deploying these applications on edge servers with limited resources poses significant challenges in terms of balancing response time, accuracy, and resource utilization.

This research aims to investigate the trade-offs between monolithic and microservice-based architectures for multi-model video analytics applications. We will leverage Apache Ray and Kubernetes to develop a comprehensive benchmarking and monitoring pipeline that systematically analyzes scaling configurations and resource utilization. Our goal is to identify strategies for reducing latency and communication overhead while handling the complexity of multi-model architectures.

Through case studies, we will implement two applications consisting of multiple ML models and explore various deployment configurations, from monolithic to fully microservice-based. We will analyze their performance under different scaling strategies and investigate the impact of model disaggregation on performance metrics during scaling.

The research will focus on addressing key challenges, including:

• Implementing effective monitoring and profiling across distributed services
• Balancing performance and resource utilization across multiple models
• Researching scalability solutions to meet strict latency requirements in edge computing scenarios

Our expected outcomes include a deeper understanding of the trade-offs between monolithic and microservice-based architectures and the development of strategies for optimizing the deployment of multi-model ML applications.

This project aims to identify, analyze, and mitigate antipatterns in AI applications that use microservices-based architectures in distributed systems. The focus will be on Apache Ray within a Kubernetes environment. The goal is to develop strategies to optimize performance and resource efficiency by addressing common antipatterns that can degrade system performance, such as inefficient communication between services, over-provisioning of resources, and poor task distribution.

To achieve this, the project will benchmark AI applications, including video surveillance and action detection systems, to understand how antipatterns emerge and affect key performance metrics like latency, throughput, and resource utilization.

Channel Description for Wireless Access Systems.

The growing interdependence between optical access networks and electrical distribution systems is a key factor in maintaining the reliability of essential services in urban environments. As cities increasingly rely on both power and digital connectivity, the resilience of these interconnected infrastructures has become more critical than ever. This thesis explores the integration of various protection schemes within Passive Optical Networks (PON) and assesses their dependability on electrical distribution networks under multiple failure scenarios.

The protection schemes examined include the Unprotected scheme, as well as more robust configurations such as Type A (Feeder Fiber Protection), Type B (Dual Parented), Type C, and Type C2, which offer varying levels of reliability. These protection strategies aim to mitigate the risks posed by network failures, ensuring uninterrupted service in urban areas where dependability is essential.

A primary goal of this thesis is to develop a generic tool that can assess the dependability of optical and electrical networks, particularly within the context of FttX (Fiber to the X) architectures. While the focus will primarily be on FttC (Fiber to the Cabinet), the tool might also explore potential applications in FttB (Fiber to the Building) and FttH (Fiber to the Home) scenarios. By using OpenStreetMap (OSM) data to model real-world urban layouts, the tool will integrate these protection schemes into a PON planning system to design both optical and electrical network topologies.

Additionally, the tool will feature a failure analysis component that simulates different failure types – such as node, edge, and disaster failures – to generate robustness metrics. These analyses will be complemented by visualizations such as robustness surfaces and statistical breakdowns, offering insights into the resilience of urban infrastructures. Through this approach, the thesis aims to enhance the overall understanding of how optical and electrical networks respond to stress, with a focus on ensuring robustness and reliability in densely populated urban settings.

This thesis aims to achieve its objectives through a combination of Python scripting, data analysis libraries, and geospatial data sources. As a geospatial data source, OSM is planned to be used, and for processing and visualizing the networks Python will be used. Key Python libraries include Folium, which is employed to plot the networks interactively on maps, and NetworkX, used for creating and analyzing the structure of complex network topologies. Pandas can be used in managing and analyzing the large datasets generated from OSM and network simulations. Visualization tools such as Matplotlib, Seaborn and Folium will be utilized to create both static and interactive visual representations of the data, including network layouts and failure impacts. Together, these tools will enable a robust analysis of network dependability across optical and electrical infrastructures.

Prerequisites

Join us in tackling one of the most pressing challenges in mobile networking—managing the growing demand for data and the need for higher performance in modern applications. As single-network connections struggle to keep up, the 3GPP's Access Traffic Steering, Switching, and Splitting (ATSSS) framework offers a solution, enabling devices to dynamically switch between and simultaneously use multiple network types like 5G, LTE, and Wi-Fi.

In this project, you will:

• Develop a cutting-edge 5G testbed that adheres to 3GPP standards.
• Integrate Multipath TCP to enable seamless communication across multiple network interfaces.
• Contribute to the optimization of mobile traffic management, enhancing both performance and reliability in next-generation networks.

This work is a unique opportunity to get hands-on experience with 5G technology and be at the forefront of mobile networking innovation.

Related Reading:

1. M. Quadrini, D. Verde, M. Luglio, C. Roseti and F. Zampognaro, "Implementation and Testing of MP-TCP ATSSS in a 5G Multi-Access Configuration," 2023 International Symposium on Networks, Computers and Communications (ISNCC), Doha, Qatar, 2023, pp. 1-6, doi: 10.1109/ISNCC58260.2023.10323859.

If you are interested in this work, please send me an email with a short introduction of yourself along with your CV and grade transcript.

Prerequisites

This thesis focuses on developing and evaluating a practical approach to phase-sensitive OTDR measurement analysis. An experimental evaluation setup including an OTDR unit capable of measuring phase-sensitive Rayleigh Scattering as well as Brillouin Scattering and compute and memory limited processor provides the basis. The work aims to answer the following questions:

• How do we achieve real-time visualization of the measurement results?
• How do we enable automated (near) real-time OTDR measurement analysis, i.e., event detection and identification?
• Can a software that does data acquisition, event detection and display be deployed directly on-board the DFOS device that has low computing power and memory? How do we minimize the hardware requirements concerning compute power, memory and storage?

In future communication systems such as 6G, in-network computing will play a crucial role. In particular, processing units within the network enable to run applications such as digital twins close to the end user, leading to lower latencies and overall better performance. For this, the execution location of these applications should be changed dynamically according to the available networking resources.

 In this thesis, the task is therefore to develop and evaluate an approach to optimize the migration of medical applications, i.e., modular application functions (MAFs), when executed in the network. The challenge of this migration lies in the connection quality during the migration and the migration of stateful applications, i.e., using memory.

 The result will be an evaluated placement approach for applications in the medical environment considering the availability.

Prerequisites

The widespread deployment of Fiber-to-the-Home (FttH) in various markets has given consumers access to high-speed data connections over the extensive optical fiber-based core networks. End users usually access these networks with wireless devices using the 802.11 standard. However, as the next generation of IoT devices with VR and AI applications enter the market, the demand for improved bandwidth and latency has increased drastically. This has led to the development of Fiber-to-the-Room (FttR) networks, which seek to extend the fiber connections closer to the terminal devices by eliminating the bottleneck caused by traditional ethernet and wireless access. Current implementations utilize a cascaded XGPON optical distribution network (ODN) in convergence with WiFi 6. The ONU at the user’s premises is replaced with a Main FttR Unit (MFU), which acts as the OLT for the Sub FttR Units (SFU) in each room using point-to-multipoint architecture. The SFUs convert the optical signals to baseband and retransmit the data as WiFi frames.

These solutions keep both MAC and PHY layers between the optical transport and WiFi completely separated without using any synergies. This introduces various limitations to the FttR setup, such as degraded data rates, jitter / latency, and high power consumption. To solve these problems, a centralized wireless - optical access network (C-WAN) has been conceptualized, where the MFU becomes a central controller to enable seamless roaming, with deterministic low latency transmission and gigabit coverage for the entire room. Within the scope of this solution, a cross-domain MAC layer between optical and WiFi has been proposed to implement dynamic resource management and scheduling. Such a centralized and converged MAC protocol must reduce overhead and optimize the framing of both physical layers to minimize the processing delay at the SFUs. This thesis aims to design and evaluate such a minimal optical access protocol with a centralized controller and test its performance in an FttR setup. The ns-3 network simulator is used within the context of this thesis to implement, simulate, and analyze the designed protocol with C++. Extensive modules that simulate both XGPON and WiFi 6 are already implemented in ns-3 and can be modified as required.

5G is the newest generation of mobile networks, allowing for higher data rates, lower latency and many new features like network slicing. Its central element is the 5G Core, which is a network of specialised Network Functions (NFs). One of these NFs is responsible for roaming connections. Roaming allows subscribers to connect to the internet via other network operators’ networks if they have a roaming agreement. Between two Public Land Mobile Networks (PLMNs) there are two standardised roaming modes: Local Break Out and Home Routed Roaming.

A major part of both roaming modes is the Security Edge Protection Proxy (SEPP), a 5G NF designed to establish and maintain a secure control plane connection between two PLMNs. Implementing it, or extending the existing implementation of Open5GS, will be an important part of this work. The SEPP is connected to other NFs in the same PLMN via Service Based Interfaces (SBIs) and to other PLMN’s SEPPs via the N32 interface.

Two SEPPs connections are divided into the N32-c and N32-f interfaces. Via N32-c, the connection is established and the security capabilities of N32-f are negotiated. All control messages between NFs of the visited and the home PLMN are transmitted via N32-f. While N32-c is secured with an end-to-end Transport Layer Security (TLS) connection, N32-f either uses the same security or, alternatively, a new 5G protocol named PRotocol for N32 INterconnect Security (PRINS). PRINS uses end-to-end application layer encryption and additionally hop-to-hop TLS encryption. While one direct TLS connection is more secure, it relies on a direct link between both parties. Considering a roaming scenario with two countries separated by multiple thousand kilometres, direct links are not always feasible. Alternatively, two PLMNs are connected via IP Exchange Networks (IPXs). To be able to route the packets reliably to their respective destinations, the IPX providers have to have access to the packets’ data. PRINS aims to provide security for this option by using the Javascript Object Signing and Encryption (JOSE) framework.

This work aims to implement N32-c and both options for the N32-f interface and investigate their differences regarding security, operability, and performance.

Prerequisites

This work aims to find the best links that, when added to the network, improve the network sovereignty.

Prerequisites

A cut set is a set of components that, by failing, causes the system to fail. A cut set is minimal if it cannot be reduced without losing its status as a cut set.

In this work, we aim to improve network availability and sovereignty based on the mincutsets. We employ graph coloring methods to improve the availability of mincutsets.

Prerequisites

In future communication systems such as 6G, in-network computing will play a crucial role. In particular, processing units within the network enable to run applications such as digital twins close to the end user, leading to lower latencies and overall better performance.

 In this thesis, the task is to develop and evaluate an approach to optimize the availability  of medical applications, i.e., modular application functions (MAFs), when executed in the network. For that, suitable real use cases are identified together with our partners at MITI (Hospital "Rechts der Isar"). The optimizing approach then leads to a specified distribution of the processing and networking resources, satisfying the minimum needs of critical applications while considering the needed availability.

 The result will be an evaluated placement approach for applications in the medical environment considering the availability.

Prerequisites

In future communication systems such as 6G, in-network computing will play a crucial role. In particular, processing units within the network enable to run applications such as digital twins close to the end user, leading to lower latencies and overall better performance.

 In this thesis, the task is to develop and evaluate an approach to minimize the power consumptions  of medical applications, i.e., modular application functions (MAFs), when executed in the network. For that, suitable real use cases are identified together with our partners at MITI (Hospital "Rechts der Isar"). The optimizing approach then leads to a specified distribution of the processing and networking resources, satisfying the minimum needs of critical applications while considering the power consumption.

 The result will be an evaluated power minimizing approach for applications in the medical environment.

Prerequisites

In future communication systems such as 6G, in-network computing will play a crucial role. In particular, processing units within the network enable to run applications such as digital twins close to the end user, leading to lower latencies and overall better performance.

 In this thesis, the task is to place medical applications, i.e., modular application functions (MAFs), in the networking considering various parameters similar to [1]. For that, suitable real use cases are identified together with our partners at MITI (Hospital "Rechts der Isar"). The optimizing approach then leads to a specified distribution of the processing and networking resources, considering various important parameters.

 The result will be an evaluated placement approach for applications in the medical environment.

[1] A. Hentati, A. Ebrahimzadeh, R. H. Glitho, F. Belqasmi and R. Mizouni, "Remote Robotic Surgery: Joint Placement and Scheduling of VNF-FGs," 2022 18th International Conference on Network and Service Management (CNSM), Thessaloniki, Greece, 2022, pp. 205-211, doi: 10.23919/CNSM55787.2022.9964591.

Prerequisites

5G is the newest generation of mobile networks, allowing for higher data rates, lower latency and many new features like network slicing. Its central element is the 5G Core, which is a network of specialised Network Functions (NFs). One of these NFs is responsible for roaming connections. Roaming allows subscribers to connect to the internet via other network operators’ networks if they have a roaming agreement. Between two Public Land Mobile Networks (PLMNs), there are two standardised Roaming modes: Local Break Out and Home Routed Roaming. For Local Break Out Roaming, only the home network’s control plane is accessed from the visited network, while the user data is directly transmitted to the Data Network (DN). For Home Routed Roaming, the user data is routed through the home network to the DN. This thesis aims to implement both Roaming versions in an open-source core network and compare them regarding chosen KPIs, e.g., latency or throughput. Open5GS would be the primary choice for the open-source core network, as it already supports Local Break Out Roaming. Home Routed Roaming is not yet supported.

A major part of 5G roaming is the Security Edge Protection Proxy (SEPP), a 5G NF designed to establish and maintain a secure control plane connection between two PLMNs. Implementing it, or extending the existing implementation of Open5GS, will be an important part of this work. The SEPP is connected to other NFs in the same PLMN via Service Based Interfaces (SBIs) and to other PLMN’s SEPPs via the N32 interface.

The biggest difference between the two roaming scenarios lies in the data plane routing, so implementing the connection between two User Plane Functions (UPFs), the N9 interface, is necessary to connect two PLMNs. The newly introduced Inter PLMN User Plane Security (IPUPS) used for additional security on this connection is initially considered out-of-scope for this work but may be added later.

A security analysis regarding control and user plane for both roaming modes finishes this work’s contributions. Potential focal points are the control capabilities of the home PLMN operator in Local Break Out Roaming.

Prerequisites

Financial exchanges consider a migration to the cloud for scalability, robustness, and cost-efficiency. Jasper presents a scalable and fair multicast solution for cloud-based exchanges, addressing the lack of cloud-native mechanisms for such.

To achieve this, Jasper employs an overlay multicast tree, leveraging clock synchronization, kernel-bypass techniques, and more. However, there are opportunities for enhancement by confronting the issue of inconsistent VM performance within identical instances. LemonDrop tackles this problem, detecting under-performing VMs in a cluster and selecting a subset of VMs optimized for a given application's latency needs. Yet, we believe that LemonDrop's approach of using time-expensive all-to-all latency measurements and an optimization routine for the framed Quadratic Assignment Problem (QAP) is overly complex. 

The proposed work aims to develop a simpler and scalable heuristic, that achieves reasonably good results within Jasper's time constraints. 

Resource constraints of edge devices serve as a major bottleneck when deploying large AI models in edge computing scenarios. Not only are they difficult to fit into such small devices, but they are also quite slow in inference time, given today's need for rapid decision-making. One major technique developed to solve this issue is Variant Parallelism. In this ensemble-based deep-learning distribution method, different main model variants are created and deployed in separate machines, and their decisions are combined to produce the final output. 

    The method provides graceful degradation in the presence of faulty nodes or poor connectivity while achieving an accuracy similar to the base model.

    However, the technique used to generate variants can fail in scalability as combining variants of smaller size with somewhat identical characteristics may not help achieve a significant accuracy boost unless they are retrained with different random seeds. Therefore, this research will focus on improving variant parallelism by exploring other ways to generate variants. We will apply knowledge distillation (KD), where a teacher model of a certain type (e.g., ResNet-50) can be used to train a smaller student model or a model of a completely different structure (e.g., MobileNet). 

    We aim to develop a variant generation technique where we can generate as many variants as there are participating devices while boosting accuracy and inference speed. Additionally, we will create an optimization scenario that dynamically creates a smaller student model based on specific requirements, such as hardware characteristics and end-to-end performance metrics.

With the increasing need for broadband in rural areas and the shift towards fiber-based Passive Optical Networks (PON), this research will focus on the deployment strategies of Balanced PON (BPON) and Unbalanced PON (UPON). The aim is to enhance efficiency and minimize infrastructure costs associated with broadband deployment. Recognizing the high expense associated with fiber deployment, especially in rural areas where geographical and demographic factors pose significant challenges, this study will focus on a shift from the conventional BPON approach, characterized by uniform power splitting (e.g., 1:16 or 1:32 splitting), to a more adaptable UPON strategy. The UPON architecture facilitates variable power splitting ratios, enhancing network reach and optimizing the distribution of network resources—such as bandwidth and optical power—in rural areas, where the distances between Optical Network Units (ONUs) or customer premises are greater than those in urban settings.

The methodology includes gathering detailed rural area data from OpenStreetMap to simulate realistic network designs, including the strategic placement of Optical Line Terminals (OLTs), splitters, and Optical Network Units (ONUs). The research will compare the traditional BPON, both single and cascading splitting, against UPON in terms of fiber length utilization, network elements placement strategy, power distribution efficiency, and overall cost-effectiveness. By using Gabriel graphs to generate rural area networks and analyzing PON equipment parameters like transmitted power range, sensitivity, and fiber attenuation, this research will be dedicated to identifying the most efficient, unprotected, optical access network configurations for rural settings. The research will also consider the potential of merging BPON and UPON strategies, aiming to harness the combined benefits of both architectures for a more versatile and cost-effective rural broadband deployment.

This comparative analysis is expected to keen insights into the scalability of BPON and UPON solutions, guiding network operators toward more informed infrastructure development decisions in rural settings. By analyzing multiple Gabriel graphs to evaluate total fiber length, splitter requirements, and infrastructure cost, this research aims to derive comprehensive guidelines for efficient fiber deployment in rural areas. These guidelines will contribute to internet access deployment strategies and help narrow the digital divide, showcasing UPON as an affordable and practical solution for rural broadband. By aligning with initiatives like the Broadband Programs in Germany and Bavaria, this research underscores the global effort to extend high-quality internet connectivity to underserved areas. [1] This analysis will thus empower network operators with the knowledge to select the most appropriate PON configuration for rural deployments, ensuring efficient, reliable, and affordable broadband access.

References:

[1] "State aid: Commission approves German scheme for very high capacity broadband networks in Bavaria." The European Sting, 29 Nov. 2019,
https://ec.europa.eu/commission/presscorner/detail/en/ip_19_6630.

Prerequisites

The dependability that the electrical and optical networks have on each other is a critical factor in ensuring uninterrupted services to the large population of the landscape of urban infrastructure. This thesis aims to delve into the complexity of different protection schemes in optical networks and explore their interconnection with electrical networks through different kinds of failures. The motivation behind this thesis comes from the increasing reliance on digital and electrical services, making the importance of the robustness of the underlying infrastructure very critical. Additionally, the focus will be on urban areas due to their large number of population and complex infrastructural needs.

Protection schemes

Optical networks are backbone technologies that provide telecommunication services to urban areas and to safeguard these networks from failures and ensure continuity, different protection schemes are implemented.

• Unprotected scheme: Basic configuration without any backup paths for the feeder fiber, leaving the network vulnerable to service disruptions.
• Type A Protection (Feeder Fiber Protection): Incorporates a backup path for the feeder fiber's working path, enhancing reliability.
• Type B Protection (Dual Parented): Each subscriber is connected to two Optical Line Terminals (OLTs) located in different geographical areas. This setup provides a secondary OLT as a backup, significantly reducing the risk of service interruption.
• Type C Protection: Offers complete redundancy with two independent links extending to the subscriber's location, ensuring the highest level of network reliability.

The initial phase of this thesis involves mapping these protection schemes onto real-world urban layouts using OpenStreetMap data and Python scripting.

Failure Analysis and Impact Assessment

A critical aspect of network dependability is understanding how networks respond to failures and for this reason, this thesis implements different types of failures.
 

• Node Failure: Focuses on the failure of Optical Line Terminals (OLTs) and Remote Nodes (RNs), excluding Optical Network Units (ONUs) since their failure impacts are isolated.
• Edge Failure: Involves simulating failures of all network connections (edges) to assess the extent of impact on ONUs.
• Disaster Failure: Examines the effects of large-scale disasters, using a defined centroid and radius, to determine the number of affected ONUs within the disaster zone.

To explain the impacts on network functionality and ONUs, for each failure type the thesis will present visualizations and statistical analysis like boxplots or histograms.
Interdependence with Electrical Networks Parallel to the exploration of optical networks, this thesis will study power network topologies, design constraints, and requirements necessary for integrating electrical network functionalities into the same urban layouts. Similar to the optical network, will simulate various power network failures and analyze their impacts on ONU connectivity. This approach aims to highlight and enhance the mutual dependability between electrical and optical networks, thereby increasing overall system robustness against failures.

All the work in this thesis is based on a combination of advanced scripting, data analysis libraries, and geospatial data sources to achieve its objectives. The primary tools and technologies used are Python, OpenStreetMap (OSM), and Python libraries. Some Python libraries are NetworkX which is used for creating and studying the structure of complex networks, and Pandas which is crucial for managing and analyzing the large datasets generated from OpenStreetMap and network simulations. Matplotlib and Seaborn to create static and interactive visualizations in Python.

Prerequisites

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.

Problem: BGP is performance-agnostic

Solution: incorporate a delay-related metric into the best-path selection process.

Approach: Estimate the round-trip prop_delay to destinations (/24s) within the routing table of the IXP

Goal: Evaluate if it is possible to outperform BGP’s route selection criterion, in terms of latency, with a measurement-based approach.

The advent of Software-Defined Networking (SDN) has revolutionized the way networks are managed and secured. In the context of optical access networks, where performance and security are paramount, it is crucial to develop advanced mechanisms for safeguarding against threats like TCP-SYN flood attacks. This research proposal aims to investigate a novel approach to thwarting such attacks, leveraging SDN controllers and programmable switches, specifically in optical access networks.

How can SDN controllers and programmable switches be employed to effectively detect and mitigate TCP-SYN flood attacks in optical access networks, utilizing authentication using a modified SYN-ACK exchange and actuating triggers, while maintaining network performance and reliability?

This research aims to contribute to the field of network security and SDN by providing a cutting-edge solution for mitigating TCP-SYN flood attacks in optical access networks. The expected outcomes include:

1. An innovative approach to SYN flood attack mitigation, leveraging SYN-ACK exchange and P4-based actuating triggers.

2. Insights into the performance and scalability of the proposed solution in optical access network scenarios.

3. A comprehensive evaluation of alternative SYN flood attack mitigation techniques, aiding network administrators in selecting the most appropriate method.

Prerequisites

Software defined networks (SDN) have made data traffic routing a lot more convenient. The functionality of the additional controller can be used e.g. for detecting network threats like DoS or also for load balancing by redirecting data traffic. The initial idea of SDNs is that each time a new packet enters the network the packet is first forwarded to the controller to be checked. The controller then decides on which route the packet shall be send inside the network or tells the network to drop the packet, for instance if it is a threat. Each of the switches then save this information in their match-action tables. However, this model cannot scale in large networks with thousands or even millions of different packets trafficking, since that would lead to an additional latency, if every single packet needs to be sent to the controller.

Therefore, a promising solution to improve the model is to include a Machine Learning algorithm into the process. Specifically, Decision Trees (DT) and Random Forests (RF) can be used to do this line-rate classification. Since Decision Trees do not require complex mathematical operations, they can be easily deployed into the programmable switches using P4 language. Either a per-packet or a per-flow approach, each with its advantages and its drawbacks, will automate the decision of the switch of how to handle the incoming traffic instead of always forwarding it first to the controller.

In this master thesis a realisation of a DT into the P4 switches will be tested. First a functioning DT based on a real data traffic dataset will be implemented. Both variations (per-packet/per-flow) will be taken into consideration. The second step will be to translate the algorithm into the P4 switches. Afterwards the prediction performance will be analysed. The final step will be to compare the ML approach to the non-ML approach and draw conclusions on the results.

Prerequisites

The master thesis aims to develop a connected sensor fusion system focusing on its application in Urban Air Mobility localization. By gathering data from multiple sensors, the air vehicles (AVs) will be able to better estimate the airspace view and improve their route planning.  The performance of IoT protocols within the context of a 6G system will be assessed. The study also seeks to evaluate the impact of network performance factors, such as delay and packet loss, on the accuracy of the fusion data. Additionally, the thesis will investigate the impact of a semantic-aware transport layer on the performance of the fusion system. Ultimately, the research not only contributes to the advancement of UAM technology but also aligns with the emerging 6G paradigm, offering a more connected and efficient solution for tactical deconfliction in airspace navigation, making it safer and more reliable.

The thesis studies a Markov model of two correlated sources (X and Y) transmitting the data over a wireless channel for remote estimation. The objective of the thesis is to develop a strong theoretical insight for modelling an estimator to optimize the errors and age of information in a wireless communication system. We aim at studying various estimation strategies to do so, and demonstrate the optimal method for the given conditions (such as correlation between the sources, and Markov model parameters). The implementation is carried out by simulating the abovementioned theoretical concepts in MATLAB under different conditions.

With the advent of 5G cellular networks, more stringent types of traffic, pertaining to applications like augmented reality, virtual reality, and online gaming, are being served nowadays. However, this comes with an increased energy consumption on both the user’s and network side, challenging this way the sustainability of cellular networks. Furthermore, the in-network computing aspect exacerbates things even further in that direction. 

Hence, it is very important to provide end-to-end sustainability, i.e., minimize the energy consumption in the network while maintaining performance guarantees, such as the maximum latency each flow should experience. This can be done, for example, depending on the traffic load in the network, and in order to keep the energy usage at low levels, the operator can decide to shut off certain network components, like User Plane Functions (UPFs) or edge clouds, and reassign the tasks to other entities. 

In this thesis, the focus will be on the core network. The aforementioned decisions will come up as solutions to optimization problems. To that end, the student will formulate optimization problems and solve them either analytically or using an optimization solver (e.g., Gurobi). The other part would be conducting realistic simulations and showing the improvements with our approach. 

Prerequisites

The analysis of a dual data-gathering strategy for Internet-of-Things (IoT) devices in status update systems offers valuable insights into improving the efficiency and reliability of data collection in IoT environments. This thesis focuses on investigating the dual data gathering strategy, aiming to optimize the performance of status update systems in IoT deployments. The dual data-gathering strategy takes advantage of both local and remote processing capabilities. Using different source servers, this strategy aims to reduce energy consumption and network congestion in status update systems. The anticipated outcomes of this research include a comprehensive understanding of the dual data gathering strategy, mathematical models to analyze its performance, and insights into its practical implementation. These outcomes will not only advance the theoretical understanding of status update systems in IoT but also have practical implications for the design and deployment of IoT networks and applications.

Kubernetes (K8s) has become the de facto standard for orchestrating containerized applications. K8s is an open-source framework which among many features, provides automated scaling and management of services. 

Considering a microservice-based architecture, where each application is composed of multiple independent services (usually each service provides a single functionality), K8s' Horizontal Pod Autoscaler (HPA) can be leveraged to dynamically change the number of  instances (also known as Pods) based on workload and incoming request pattern.

The main focus of this project is to benchmark the HPA behavior of a Kubernetes cluster running a microservice-based application having multiple services chained together. That means, there is a dependency between multiple services, and by sending a request to a certain service, other services might be called once or multiple times.

This project aims to generate incoming request load patterns that lead to an increase in either the operational cost of the Kubernetes cluster or response time of the requests. This potentially helps to identify corner cases of the algorithm and/or weak spots of the system; hence called adversarial benchmarking.

The applications can be selected from commonly used benchmarks such as DeathStarBench*. The objective is to investigate on the dependencies between services and how different sequences of incoming request patterns can affect each service as well as the whole system.

* https://github.com/delimitrou/DeathStarBench/blob/master/hotelReservation/README.md

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.

Prerequisites

The introduction of 5G technology is transforming the telecommunications industry, offering enhanced connectivity and supporting advanced use cases such as IoT, ultra-reliable low-latency communications, and enhanced mobile broadband. 

A key challenge in this ecosystem is enabling seamless 5G roaming between different mobile network operators (MNOs) across borders, which requires reliable interconnection via IP eXchange (IPX) networks.

This research internship aims to explore the feasibility of using Mininet, a network emulation tool, in conjunction with Open5GS, an open-source 5G core network implementation, to simulate basic IPX functionalities for supporting 5G Standalone (SA) roaming use cases. 

The focus will be on setting up the system, adding support for needed protocols and integrating the Mininet-IPX-setup into the current LKN 5G Roaming Testbed.

Prerequisites

Containerizing the ROS2 components according to the MAF concept.

Analyzing traffic in today's communication networks becomes more and more complex due to the increasing heterogeneity of networked devices and general rising traffic volumes. 
In this context, data-driven methods can facilitate a deep understanding of the inherent dynamics needed to operate such networks efficiently.

Because network-related data is often naturally represented in graph form, this Internship specifically seeks to evaluate how methods from the Pytorch library for temporal graph learning, "PyTorch Geometric Temporal," can be applied to IP-to-IP level data.  

Goal is to among others to predict:

• IP-node activity over time 
• IP-to-IP level communications (e.g. number of packets) over time

Steps:

1. Set up a working ML pipeline.
2. Fine-tune models / try different models.
3. Identify promising directions and limitations
4. Conclude what needs to be done/tried in the future.

Prerequisites

Molecular communication (MC) is a novel communication paradigm envisioned to enable revolutionary future medical and biological use cases such as in-body networks for the diagnosis and treatment of diseases. MC is based on the transport of molecules for information exchange and represents a very energy-efficient and bio-compatible communication mechanism on the centimeter to nanometer scale. The communication nodes can be very small as they will be based on artificial cells or other types of tiny nano-machines.

In order to realize complex applications, such as targeted drug delivery or the detection and localization of infections and tumors, nano-machines must cooperate and communicate. The specific properties and mechanisms in biological environments and at very small scales lead to several challenges:

• Novel channel models and conditions based on diffusion and flow of molecules.
• Extremely slow speeds compared to electromagnetic waves.
• Highly stochastic behavior of the molecules.
• Low capability of future nano-machines, not able to conduct complex computations or sophisticated algorithms.

Therefore, research on MC networks is crucial to enable a future internet of bio-nano things (IoBNT) integrating classical and molecular networks.

In this internship, the student will work with experimental data from an MC testbed to implement, evaluate, and improve detection algorithms. The considered algorithms range from simple to complex, including symbol-by-symbol detection, sequence detection, matched filters, etc. The goal is to identify the strengths and weaknesses of various algorithms with respect to the characteristics of the MC signal at the receiver.

Optical networks are crucial for digital communications, handling massive data transport over long distances with wavelength division multiplexing (WDM) in the C-band. However, growing traffic demands may surpass conventional WDM capacities, prompting the exploration of ultra-wideband (UWB) and spatial division multiplexing (SDM) solutions. SDM increases throughput by utilizing different spatial domains, while UWB increases the available spectrum by taking advantage of transmission over multiple bands. Despite hesitancy due to cost, network operators recognize the need for optimal topology upgrades to meet evolving traffic demands, especially when reliability is taken into account. On top of the SDM and UWB upgrades on the existing topology, this also includes installing new links that do not exist in the original topology.

In this thesis, the goal is to optimally add links to existing core network topologies using the aforementioned schemes (SDM and UWB), according to increasing traffic demands, towards minimum cost, and considering reliability constraints, such as dedicated 1+1 protection for the demands.

Prerequisites

6G soll als neuer und zukünftiger Mobilfunkstandard den Menschen in den Fokus rücken. Neben sehr geringen Latenzen und extrem großen Datenübertragungsraten wird das Netzwerk zuverlässiger, sicherer und dynamischer. Diese Eigenschaften sind besonders in der Medizin und der Medizingerätetechnik gefragt, um intelligente Datenübertragung zwischen Geräten und Menschen zu ermöglichen. Im Rahmen des Verbundprojektes „6G-life“ sollen zwei medizinische Demonstratoren entwickelt werden, die diese Netzwerkeigenschaften untersuchen. Ein Demonstrator ist ein robotisches Telediagnostiksystem zur Remote Untersuchung von Patienten. Ein zweiter Demonstrator beschäftigt sich mit der raumadaptiven Erfassung von Vitalparametern.

Ziel der Forschungspraxis ist die Ermittlung von medizintechnischen Anforderungen an die Netzwerkkommunikation hinsichtlich der Ausfallsicherheit. Dies beinhaltet eine Recherche gängiger Normen und Richtlinien, die bei der medizinischen Datenübertragung berücksichtigt werden müssen, sowie Maßnahmen, die zur Sicherstellung zuverlässiger Kommunikation getroffen werden können. Darüber hinaus sind Interviews mit Medizinern wünschenswert, um die Nutzerseite zu berücksichtigen.

Im Rahmen der Arbeit sollen folgende Punkte behandelt werden:

• Recherche zu gängigen Richtlinien und Mechanismen, die in der medizintechnischen Netzwerkkommunikation zur Ausfallsicherheit eingesetzt werden.
• Interviews mit Klinikpersonal
• Evtl. Messung mit den vorhandenen Testbeds

Diese FP wird in Kooperation mit unseren Partnern der MITI Gruppe am Krankenhaus "Rechts der Isar" durchgeführt.

Prerequisites

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.

Prerequisites

Serverless computing is a cloud computing paradigm that separates infrastructure management from software development and deployment. It offers advantages such as low development overhead, fine-grained unmanaged autoscaling, and reduced customer billing. From the cloud provider's perspective, serverless reduces operational costs through multi-tenant resource multiplexing and infrastructure heterogeneity.

    However, the serverless paradigm also comes with its challenges. First, a systematic methodology is needed to assess the performance of heterogeneous open-source serverless solutions. To our knowledge, existing surveys need a thorough comparison between these frameworks. Second, there are inherent challenges associated with the serverless architecture, specifically due to its short-lived and stateless nature.

The research internship aims to implement a reinforcement learning (RL) algorithm using Python to optimize routing agents in QKD networks. The literature showed different proof of concepts from other applications, and the research internship's objective is to adapt the best-suited one to the characteristics of QKD. 

Prerequisites

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 rapid growth of edge computing has introduced new challenges in managing and scaling workloads in distributed environments to maintain stable service performance while saving resources. To address this, this research internship aims to explore the feasibility and implications of extending the AWARE framework (Qiu et al., 2023) [1], which has been developed by as an automated workload autoscaling solution for production cloud systems, to edge environments. 

 AWARE utilizes tools such as reinforcement learning, meta-learning, and bootstrapping when scaling out workloads in the horizontal dimension by increasing the number of deployment instances and scaling up in the vertical dimension by increasing the allocated resources of a deployment instance. We will employ edge environment infrastructures with limited resources that run a lightweight distribution of the Kubernetes (K8s) container orchestration tool, and the goal is to gain insights into the performance, adaptability, and limitations of this approach.

In this work, we will evaluate several additional techniques in 5G/6G toward reliability enhancements focusing on the Radio Access Network (RAN). The student is expected to first understand and evaluate the concept via simulations over MATLAB. Then, the techniques will be implemented in OpenAirInterface (OAI) [1] platform and we will evaluate the enhancements over our practical 5G testbed setup.

The initial setup will include a mobile robot, 5G Stand-alone communication, and a multi-access edge computing (MEC) system running a machine learning algorithm.

The expected outcome is to have improvements to the RAN of OAI including but not limited to wireless channel estimation and equalization, downlink reliability. More details will be provided after the first meeting.

[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.

Prerequisites

This work focuses on the exploration of networks enabling train control and on-board data communications under mobility scenarios. Today, low bandwidth networks such as GSM, providing less than 200 Kbps are being used to transmit train control information. Moreover, despite trains may use multiple on-board technologies to provide users with an internet connection (e.g., repeaters, access points), they fail in their attempt as these connections are characterized by having low throughput (less than 2 Mbps) and frequent service interruptions.

This work aims at the development of a network planning solution enabling future applications in train mobility scenarios such as: Automatic Train Operation (ATO) [1,2], leveraging cloud technologies and meeting bandwidth requirements of data-hungry end-users' applications. Here, special attention will be given to the migration of communications services triggered by trains mobility patterns. It is expected of the student to find solutions to the following questions:

• When to trigger service migrations?

• Where to migrate services? (i.e., to which data center)

• How to handle this process? (So that the user does not perceive any interruption)

 Given:

• Trains mobility patterns

• Service requirements in terms of bandwidth and delay

• Network topology

• Data center locations

 
The results from this work can be useful to get an insight on requirements for Smart Transportation Systems, that may in turn be useful for cementing the basis of other scenarios such as: Autonomous Driving and Tele-Operated Driving.

 [1] Digitale Schiene Deutschland. Last visit on 13.12.2021 https://digitale-schiene-deutschland.de/FRMCS-5G-Datenkommunikation

[2] 5G-Rail FRMCS. Last visit on 13.12.2021 https://5grail.eu/frmcs/

Prerequisites

This work focuses on the exploration of networks enabling train control and on-board data communications under mobility scenarios. Today, low bandwidth networks such as GSM, providing less than 200 Kbps are being used to transmit train control information. Moreover, despite trains may use multiple on-board technologies to provide users with an internet connection (e.g., repeaters, access points), they fail in their attempt as these connections are characterized by having low throughput (less than 2 Mbps) and frequent service interruptions.

This work aims at the development of a network planning solution enabling future applications in train mobility scenarios such as: Automatic Train Operation (ATO) [1,2], leveraging cloud technologies and meeting bandwidth requirements of data-hungry end-users' applications. Here, special attention will be given to the migration of communications services triggered by trains mobility patterns. It is expected of the student to find solutions to the following questions:

• When to trigger service migrations?

• Where to migrate services? (i.e., to which data center)

• How to handle this process? (So that the user does not perceive any interruption)

 Given:

• Trains mobility patterns

• Service requirements in terms of bandwidth and delay

• Network topology

• Data center locations

 
The results from this work can be useful to get an insight on requirements for Smart Transportation Systems, that may in turn be useful for cementing the basis of other scenarios such as: Autonomous Driving and Tele-Operated Driving.

 [1] Digitale Schiene Deutschland. Last visit on 13.12.2021 https://digitale-schiene-deutschland.de/FRMCS-5G-Datenkommunikation

[2] 5G-Rail FRMCS. Last visit on 13.12.2021 https://5grail.eu/frmcs/

Prerequisites

Clustering is already used in our cities to distribute splitters for managing network operations. However, the current distribution does not effectively reduce the costs caused by huge urban area with long fiber lengths. To solve this, we can use k-means clustering to group the splitters and optimize how fibers are laid out in the city.

With k-means clustering, we group users (ONUs) in an area to create a virtual center. This center is chosen so that the total distance to all users in the area is minimized. We then find a nearby point where a splitter can be placed, ensuring the distance from the splitter to the users is as short as possible.

Next, we cluster the remote nodes (RNs) where splitters are located. By grouping nearby splitters, we can combine their connections into one line to the central office (OLT). This line will be organized by a new splitter near to the center of the old splitter's cluster center. This helps reduce the total fiber length needed and lowers deployment costs.

Prerequisites

...

The Internship aims to analyze Open Source Optical Network Data for performance prediction of Optical Networks and develop data-driven methods for quality of transmission estimation.

Deterministic, low-latency communication for time-sensitive data is a critical challenge in industrial and automotive networks. IEEE introduced the Ethernet standards Time-Sensitive Networking (TSN) to address these requirements. Using traffic prioritization and scheduling, TSN enables deterministic, reliable, and low-latency communication. However, industry 4.0 has intensified the need for mobility, such as using Automated Guided Vehicles (AGVs). To meet these demands, 5G must be integrated into TSN systems.

Your role will involve enhancing and further developing a TSN-5G testbed. A baseline testbed integrating 5G into TSN is already in place. The next phase of development focuses on integrating reliability mechanisms. Your specific tasks will include:

• Measuring and analyzing end-to-end delays.
• Setting up and integrating additional devices, such as UEs, into the testbed.
• Integrating a network controller into the system.
• Adapting the controller to support additional mechanisms.

What you’ll gain:

• Experience with networking hardware.
• Expertise in hardware measurements.
• Practical experience in system implementation.

Depending on your availability, your working hours per week can be between 10 and 20. If this opportunity interests you, please send us a brief introduction about yourself along with your CV and transcript of records. We look forward to meeting you!

Prerequisites

5G is the newest generation of mobile networks allowing for higher data-rates, lower latency and many new features like network slicing. Its central element is the 5G Core, which is a network of specialised Network Functions (NFs). Roaming allows subscribers to connect to the internet via other network operator’s networks if they have a roaming agreement. We are looking for a student to help implement and maintain a 5G Roaming testbed. At first, that is planned as an open source testbed leveraging Open5GS. Later, the plan is to connect this open source testbed to the LKN campus network.

This working student position may run parallel to Master Theses with more focused implementation and evaluation works. The working student is welcome to follow up on this work with his/ her own research internship or Master’s thesis.

Objectives

The primary objective of this work is to help implement and maintain a 5G Roaming testbed. This testbed shall then be used for investigation of security mechansims and performance measurements. Those are not the main job of the student, but the student is supposed to help.

1. Work into 5G Roaming

2. Implement missing Roaming functionalities into Open5GS

3. Maintain Roaming Testbed

4. Connect open source 5G Roaming testbed with Campus Network (once possible)

5. Aid in security investigations

6. Aid in performance measurements

7. Potentially add other NFs later

Prerequisites

Future medical applications put stringent requirements on the underlying communication networks in terms of highest availability, maximal throughput, minimal latency, etc. Thus, in the context of the 6G-life project, new networking concepts and solutions are being developed.

For the research of using 6G for medical applications, the communication and the medical side have joined forces: While researchers from the MITI group (Minimally invasive Interdisciplinary Therapeutical Intervention), located at the hospital "Rechts der Isar", focus on the requirements of the medical applications and collecting needed parameters of patients, it is the task of the researchers at LKN to optimize the network in order to satisfy the applications' demands. The goal of this joint research work is to have working testbeds for two medical testbeds located in the hospital to demonstrate the impact and benefits of future 6G networks and concepts for medical applications.

Your task during this work is to implement the communcation network for those testbeds. Based on an existing open-access 5G network implementation, you will implement changes according to the progress of the current research. The results of your work, working 6G medical testbeds, will enable researchers to validate their approaches with real-world measurements and allow to demonstrate future 6G concepts to research, industry and politics.

In this project, you will gain a deep insight into how communication networks, especially the Radio Access Network (RAN), work and how different aspects are implemented. Additionally, you will understand the current limitations and weaknesses as well as concepts for improvement. Also, you will get some insights into medical topics if interested. As in such a broad topic there are many open research questions, you additionally have the possibility to also write your thesis or complete an internship.

Prerequisites