Studentische Arbeiten

The next generation of communication networks, namely 6G, will enable a huge variety of medical applications. In the current phase, our research focuses on optimizing the interaction between network and medical application developing. For this, we joined forces with the Minimally Invasive Interdisciplinary Therapeutical Intervention (MITI) group, sitting at the hospital "Rechts der Isar".

This research internship focuses on one certain use case, the mobile patient monitoring of patiens in the operation room. In particular, the vital parameters of the patient are continously measured and sent to a server. The task of the research internship is to develop and implement the communication protocol between the sensors and the processing server. Hereby, a basic implementation is already given. However, key components are still missing, which should be implemented and evaluated within the scope of this research internship.

Please note, that the real setup is located at MITI. You may have to work there.

• Motivation
• Knowledge about Python and Linux
• Basic communication (protocol) principles

Emerging medical applications put stringent requirements on the underlying communication infrastructure. Thus, for future communication networks, namely 6G, modern concepts are researched and implemented.

In order to set up a demonstrator for medical applications together with our partners from MITI at the hospital "Rechts der Isar", the Radio Access Network (RAN) has to be implemented. Then, real medical applications can run within the network.

The goal of this Forschungspraxis is to implement an existing 5G Open-Source RAN (OpenAir Interface) on real hardware and evaluate its performance.

• Most important: Motivation
• Of advantage is knowledge in Python and C as well as basic communication techniques and 5G

We search for a student to analyze the monitoring data of QKD devices deployed in a realistic environment. The monitoring data is already acquired and offers the possibility to investigate the relation between reported performance, vendor specifications, and the influence of the environment. The goal is to gain insights into performance prediction, eavesdropping detection and influences on security by applying data science, including AI/ML.

• Programming skills in Python
• Experience in data science and statistics
• Foundation in information theory
• Interest in security and practical concepts of guaranteed security
• Willingness to analyze and understand imperfections of quantum transmission

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. 

- Basic understanding of 5G Core Networks and Mobile Edge Computing (MEC).

- Experience with mathematical formulation of optimization problems.

- Programming experience with Python and Gurobi.

Molecular communication (MC) is an alternative to classical electromagnetic wave-based communication, where molecules are used for information exchange. MC is expected to enable in-body networks for future medical applications in the Internet of Bio-Nano Things, a vision for 6G and beyond.

We seek a working student to help us set up and maintain a molecular communication networks testbed at the chair. The testbed will be based on ink molecules transmitted through a water-filled tube system with a background flow. We plan on using spectral absorption measurements to detect the information molecules.

What you will do:

• Set up the tubes, pumps, sensors, and other components
• Program microcontrollers to control microfluidic pumps and spectral sensors
• Debugging and fault detection in both software and hardware
• Implement data collection methods

What you need:

• Interest in future and unconventional communication methods
• Willingness to learn new things and a hands-on mentality
• Experience in (low-level) programming (e.g., C/C++), preferably with microcontrollers/Arduinos for controlling sensors

Nice to have:

• Already worked with spectral sensors
• Experience with CAD, 3D printing, and soldering

    In recent years, deep learning-based algorithms have demonstrated superior accuracy in video analysis tasks, and scaling up such models; i.e., designing and training larger models with more parameters, can improve their accuracy even more.

    On the other hand, due to strict latency requirements as well as privacy concerns, there is a tendency towards deploying video analysis tasks close to data sources; i.e., at the edge. However, compared to dedicated cloud infrastructures, edge devices (e.g., smartphones and IoT devices) as well as edge clouds are constrained in terms of compute, memory and storage resources, which consequently leads to a trade-off between response time and accuracy. 

    Considering video analysis tasks such as image classification and object detection as the application at the heart of this project, the goal is to evaluate different deep learning model distribution techniques for a scenario of interest.

This project considers an environment consisting of several low performance machines which are connected together across a network. 

Edge AI has drawn the attention of both academia and industry as a way to bring intelligence to edge devices to enhance data privacy as well as latency. 

Prior works investigated on improving accuracy-latency trade-off of Edge AI by distributing a model into multiple available and idle machines. Building on top of those works, this project adds one more dimension: a scenario where $f$ out of $n$ contributing nodes are adversary. 

Therefore, for each data sample an adversary (1) may not provide an output (can also be considered as a faulty node.) or (2) may provide an arbitrary (i.e., randomly generated) output.

The goal is to evaluate robustness of different parallelism techniques in terms of achievable accuracy in presence of malicious contributors and/or faulty nodes.

Note that contrary to the mainstream existing literature, this project mainly focuses on the inference (i.e., serving) phase of deep learning algorithms, and although robustness of the training phase can be considered as well, it has a much lower priority.

Deep Learning models are becoming increasingly larger so that most of the state-of-the-art model architectures are either too big to be deployed on a single machine or cause performance issues such as undesired delays.

This is not only true for the largest models being deployed in high performance cloud infrastructures but also for smaller and more efficient models that are designed to have fewer parameters (and hence, lower accuracy) to be deployed on edge devices.

    That said, this project considers the second environment where there are multiple resource constrained machines connected through a network. 

    Continuing the research towards distributing deep learning models into multiple machines, the objective is to generate more efficient variants/submodels compared to existing deep learning parallelism algorithms.  

Note that this project mainly focuses on the inference (i.e., serving) phase of deep learning algorithms, and although efficiency of the training phase can be considered as well, it has a much lower priority.

Deep Learning models are becoming increasingly larger so that most of the state-of-the-art model architectures are either too big to be deployed on a single machine or cause performance issues such as undesired delays. 

This is not only true for the largest models being deployed in high performance cloud infrastructures but also for smaller and more efficient models that are designed to have fewer parameters (and hence, lower accuracy) to be deployed on edge devices.

That said, this project considers the second environment where there are multiple resource constrained machines connected through a network. 

When distributing deep learning models across multiple compute nodes, trying to realize parallelism, certain algorithms (e.g., Model Parallelism) are not able to achieve the desired performance in terms of latency, mainly due to (1) communication cost of intermediate tensors; and (2) inter-operator blocking.

This project consists of multiple sub-projects each can be taken separately.

In the context of Model Parallelism, two potential modifications can be considered: 

• Pipeline parallelism by delaying the inference of the first few data samples assuming a live stream of input data.
• Finding certain points in deep learning architectures or modifying the architecture itself so that for each data sample, it becomes possible to filter out some sub-parts of the model, and therefore reducing the transmitted data, and still achieve comparable accuracy.

Class and Variant Parallelism improve inter-node communication significantly. However, the input data needs to be shared between contributing nodes. The goal is to propose a technique to transmit less data, and to find a good trade-off between computation and communication.

Note that this project mainly focuses on the inference (i.e., serving) phase of deep learning algorithms, and although efficiency of the training phase can be considered as well, it has a much lower priority.

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

The goal of the thesis would be to build an Anticipatory or Proactive Wireless Resource Allocation Framework to optimize Multi-hop, Multi-path networks.

The approach is to develop and solve an optimization problem to allocate network resources to users by looking into window of time in the future. By knowing the channel quality of the users in the future, a better, more optimal allocation of resources is made possible.

Related Reading:

Dastgheib, Mohammad Amir, et al. "Mobility-aware resource allocation in VLC networks using T-step look-ahead policy." Journal of Lightwave Technology 36.23 (2018): 5358-5370.

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.

• Strong Python programming skills
• Strong foundation on wireless communications
• Experience with optimization problems

The goal of the thesis would be to build a Wireless Resource Allocation Framework to optimize Multi-hop, Multi-path networks.

The approach is to develop an optimization problem to allocate network resources to users for task offloading and to solve this problem using Multi-agent Reinforcement Learning.

Related Reading:

Z. Cao, P. Zhou, R. Li, S. Huang and D. Wu, "Multiagent Deep Reinforcement Learning for Joint Multichannel Access and Task Offloading of Mobile-Edge Computing in Industry 4.0," in IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6201-6213, July 2020, doi: 10.1109/JIOT.2020.2968951.

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.

• Strong Python programming skills
• Strong foundation on wireless communications
• Experience with Reinforcement Learning

The increase in bandwidth requirements triggered by new services and much more terminals force network providers to upgrade their networks constantly. Obviously, the upgrade takes cost into account, but it should also consider bandwidth, delay, reliability, and security.

This master thesis will aim at modeling, implementing, and evaluating different access network architectures with a network planning tool. For that purpose, different tasks will be considered:

    • Define different network architectures based on the state of the art: combining PON and aggregation networks, considering dependabilities, etc.

    • Learn and select the best data and planning tool: ArcGIS, Gabriel Graphs, etc.

    • Implement planning solution: component placement, fiber/cable layout, etc. using ILP

    • Implement planning solution: component placement, fiber/cable layout, etc. using ML

    • Evaluate and compare the availability and cost of different architectures.

Example of previous work

Shahid, Arslan; Mas Machuca, Carmen: Dimensioning and Assessment of Protected Converged Optical Access Networks. IEEE Communications Magazine Vol. 55, No. 8, 2017

Python, ML, and ILP formulation.

Internetkommunikation offers the opportunity to develop interesting software solutions for technical questions about internet protocols and mechanisms. For the next semester, a position is available to assist the teaching assistants for the tutorials and class project.

• Knowledge in computer networking

• Good programming skills in Python

Starting with 5G, assigning portions of network resources depending on the use case, where users running the same application/service would be getting resources from the same resource pool, a process known as network slicing, introduced a paradigm shift in how cellular networks operate in general. This brought significant advantages to both users and operators, by easing the resource allocation process and improving performance.

In 5G, users can be categorized, based on the service they are running, into three broad groups: eMBB, URLLC, and mMTC. There would be a separate Radio Access Networks (RAN) slice for each group. So, all eMBB users within the same cell would receive their resources from the same slice. URLLC service type is characterized by the most stringent traffic requirements. So, different amount of resources are needed to enable a satisfying user experience for different types of services. Hence, the problem of proper slice dimensioning arises as a very important issue that needs to be addressed.

Besides splitting RAN resources (slices), given that in-network computing will become an inevitable part of next generation of cellular networks, splitting computing resources would be very important. The computing resources come from edge clouds, which are collocated with base stations. Also, users have different channel conditions, which needs to be taken into account. For each admitted user, the traffic requirement would have to be satisfied.

Therefore, the operator would need to decide how to slice the RAN resources, and given the available computing resources, it would need to decide on the number of users of each service type to admit so that its overall revenue is maximized. The candidate needs to formulate an optimization problem. Then, different static and dynamic policies are to be analyzed in order to determine the best one, depending on the computational complexity introduced and the achievable performance in terms of the objective of the optimization problem.           

A good knowledge of any programming language is required.

The slice dimensioning for the three types of traffic in 5G(eMBB, URLLC, and mMTC) would be the focus of this thesis. Each user, depending on the type of traffic, is characterized by a weight. This could be e.g., the gain of the operator by serving a given user. We assume that the more stringent the traffic requirement is, the higher the gain for the operator is. In this way, mMTC users weight would be the lowest, whereas the URRLC’s the highest. Also, users have different channel conditions, which needs to be taken into account. For each admitted user, the traffic requirement would have to be satisfied. The problem would then reduce to deciding what sizes of network slices are to be allocated to each service type, and the corresponding number of users within the slice, so that the utility for the operator is maximized.

Three policies are to be analyzed. The first is motivated by the finite number of PhysicalResource Blocks (PRBs) each cell has, so that a brute-force solution is found. The computational complexity of this policy has to be obtained as well. The second, less complex policy, is the one in which resources are reserved beforehand (i.e., it is a static policy), based depending on the ratio between the weight and the resources needed for a user of the given service type. Finally, the third policy, which would be based on a heuristic, would decide onthefly on how to dimension RAN slice sizes. 

A good knowledge of any programming language is required.

The Prgrammable Communication Networks lab offers the opportunity to work with interesting and entertaining projects related to the Software-Defined Networking (SDN) and Programmable Data Planes (PDP) paradigms. For the next semester, a position is available to assist on the lab doing the student supervision during the lab sessions.

• Solid knowledge in computer networking (TCP/IP, SDN, P4)
• Solid knowledge of networking tools and Linux (iperf, ssh, etc)
• Good programming skills: C/C++, Python

How to purify multiple entanglement optimally?

What are the different models for quantum entangled photon sources and how can they be implemented in a quantum networks simulator?

Development of a performance test framework for srsRAN and evaluating the performance using this tool.

• Development of a testsetup for network performance measurements including cameras
• Evaluation of different streaming codecs in terms of network performance

Software-Defined Networking (SDN) is a network paradigm where control and data planes are decoupled. The control plane consists on a controller, which manages network functionality and can be deployed in one or multiple servers. The data plane consists on forwarding devices which are instructed by the controller on how to forward traffic.

P4 is a domain-specific programming language, which can be used to define the functionality of forwarding devices as virtual or hardware switches and SmartNICs.

This work consists on performing measurements for a given P4 code on different devices. For that, an small P4-enabled virtual network will be used to perform some measurments. Later, data will be also collected from hardware devices as switchs and SmartNICs. Measurement should be depicted in a GUI for its subsequent analysis.

Basic knowledge on the following:

• Linux
• Networking/SDN
• Python/C
• Web programming (GUI).

Please send your CV and transcript of records.

Implement a quantized neural network in P4 and evaluate the throughput of feed-forward networks and networks with attention mechanisms on P4 hardware.

Cloud Native deployments of the 5G Core network are gaining increasing interest and many providers are exploring these options. One of the key technologies that will be used to deploy these Networks, is Kubernetes (k8s).

In 5G, NG Application Protocol (NGAP) is used for the gNB-AMF (RAN-Core) communication. NGAP uses SCTP as a Transport Layer protocol. In order to load balance traffic coming from the gNB towards a resilient cluster of AMF instances, a L4 load balancer needs to be deployed in the Kubernetes Cluster.

The goal of this project is do develop a SCTP Load Balancer to be used in a 5G Core Network to aid the communication between the RAN and Core.
The project will be developed using the language Go (https://golang.org/).

- General knowledge about Mobile Networks (RAN & Core).
- Good knowledge of Cloud Orchestration tools like Kuberentes.
- Strong programming skills. Knowledge of Go (https://golang.org/) is a plus.

Software-Defined Radio Access Network (SD-RAN) is receiving a lot of attention in 5G networks, since it offers means for a more flexible and programmable mobile network architecture.

The heart of the SD-RAN architecture are the so called SD-RAN controllers. Currently, initial prototypes have been developed and used in commercial and academic testbeds. However, most of the solutions only contain a single SD-RAN controller. Nonetheless, a single controller becomes also a single point of failure for a system, not only due to potential controller failures but also due to a high load induced from the devices in the data plane.

To this end a multi-controller control plane often becomes a reasonable choice. However, a multi-controller control plane renders the communication among the controllers more challenging, since they need to often exchange control information with each other to keep an up to date network state. Unfortunately, currently there is no protocol available for such a communication.

The aim of this work is the development and implementation of an East/West API for SD-RAN controller communication according to 5G stardardization. The protocol should enable the exchange of infromation among the SD-RAN controllers regarding UEs, BSs, wireless channel state and allow for control plane migration among controllers.

• Experience with programming languages Python/C++.
• Experience with socket programming.
• Knowledge about SDN is a must.
• Knowledge about 4G/5G networks is a plus.

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Das Thema dieser Bachelorthesis ist die Entwicklung, Optimierung und Evaluation des proprietären Brandmeldebussystems LSNi1 der Firma Bosch Sicherheitssysteme GmbH in Grasbrunn.

Brandmeldeanlagen sind ein bestehender notwendiger Teil der Gebaudetechnik und sollen fur neue vernetzte IoT-Dienste verwendet werden. Aufgrund immer höheren Anforderungen dieser Dienste soll die Datenrate uber den Brandmeldebus gesteigert werden. Zur Zeit wird das Bussystem zur verhaltnismäßig datenarmen Alarmabfrage und Antwort der Netzelemente verwendet. Ein wichtiger Punkt ist dabei, die Systemcharakteristika des Brandmeldesystems nicht zu verschlechtern. Solche Netzwerke sind als Sicherheitssysteme deklariert und mussen daher äußerst zuverlässig in Sachen Ausfallsicherheit und Fehlererkennung sein.

Die Aufgabe besteht darin, eine neue Übertragungstechnik mit höherer Datenrate auf der physikalischen Schicht des Busses zu finden und diese mit Hilfe eines Prototypen umzusetzen. Die Optimierung des Prototypen soll durch Berechnung und Experimentieren mit Systemelementen ausgearbeitet werden. Daraufhin werden verschiedene Testaufbauten, im firmeneigenen Labor getestet. Die dabei aufgenommenen Daten werden über ein entwickeltes Matlab-Evaluierungsprogramm ausgewertet, um eindeutige Aussagen über die Funktionalitat des Systems zu geben. Eine besondere Herausforderung bei Systemen dieser Art sind die hohen Kabellängen und die große Anzahl an Systemelementen auf dem Bus. Mit den ausgewerteten Ergebnissen soll überpruft werden, in welchem Umfang die Datenrate auf dem Bus gesteigert werden kann.

Linkability of Failure Messages (LFM) is a security hole in the Authentication and Key Agreement (AKA) procedure.

The LFM flaw was first reported in 3G [2] and it has also been proven to work in 5G [1]. Compared to IMSI catchers, the use of the flaw for identifying nearby subscribers has two limitations: First, the attacker has to know the ID of a person of interest that they are looking for. Only these subscribers with known IDs can be detected, it is not possible to find the ID of a new subscriber without knowing / guessing it.

Second, LFM requires an attacker to probe every new device that connects to their fake base station for every ID that they are looking for. In addition to probing every new device, the attacker also needs to contact an authentic mobile network to obtain authentication requests for each person of interest.

Due to these limitations, the LFM flaw is less powerful than previously used IMSI catchers. The objective of this project is to examine the scalability and practicability of exploiting the flaw on a larger scale.

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.

• Machine Learning
• Python and P4 programming
• Knowledge of Softrware-Defined Networking

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.

• Machine Learning
• Python and P4 programming
• Knowledge of Softrware-Defined Networking

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.

Signaling storm is a specific type of DDoS attack, which emerges from frequent small-scale signaling activities of a group of compromised UE. Typically, signaling messages are exchanged between UE and the network for establishing communication sessions and managing network resources. However, signaling attacks abuse regular procedures to generate high number of signaling messages within a short period. The generation of excessive signaling load increases the network congestion and consumes resources. In 5G, UEs must send a request to initiate themselves and establish the communication with the 5G core. These initial registration request messages contain UE related information about identity, location and capabilities. The recent research internship focused on signaling storms has revealed that an initial registration request flood can generate significant signaling load and stress the network core. In the scope of mentioned internship, a simulation environment was created using UERANSIM and open5GS to investigate the impact of repetitive initial registration requests from a botnet comprising hundreds of UEs on control plane resources. The master thesis involves a comprehensive research study based on this initial observation to identify other signaling attack scenarios initiated by UEs, that abuse regular UE signaling for registration processes, inter-slice handovers and mobility handovers. Furthermore, assessing the impact of these scenarios and exploring possible detection methodologies are crucial for the intended study.

Motivation: 5G networks are designed to be used for three types of connected services: Enhanced Mobile Broadband(eMBB), Ultra Reliable Low Latency Communications (URLLC) and Massive Machine Type Communications (mMTC). Higher throughput, reliable connections and low latency capabilities of 5G networks should meet uninterrupted and robust data exchange requirements of users. Both the industry and individual users heavily rely on seamless connectivity. However, numerous studies have shown that 5G networks are vulnerable to signaling threats and DDoS attacks, which are becoming more severe due to the growing number of mobile and IoT devices. Such attacks can increase latency and impact service availability. The majority of literature on this topic examines potential 5G threats including signal storms and their effect on users. Even some detection and prevention techniques have been proposed. Although these studies provide valuable information about signaling storms, it has not been particularly investigated how control plane resources can be exploited by flooding UE initiated and 5G protocol specific requests. The research gap regarding concrete statements to reproduce signaling attacks is the main motivation of this study.

Objectives and Research Question: This work will focus on UE initiated DDoS attacks targeting control plane resources of 5G networks and it will question if these attacks can have a severe impact on practical 5G test setup. Therefore, signaling procedures particularly the ones involving NAS and NGAP protocols, will be explored to identify scenarios for UE initiated signaling attacks. The characteristics of the identified scenarios will be derived by theoretical analysis. The remaining objectives are reproducing these scenarios conducting experiments with appropriate simulation tools, evaluating the impact of these attacks on the network and user experience and investigating detection solutions for signaling storms.

Challenges: The identified scenarios should be demonstrated and analyzed to study the research question, which poses two main challenges. Designing a simulation environment for realistic attack reproduction is elaborate, which requires determining the most suitable solution to simulate UE, gNB and 5GC among existing solutions. The simulation environment cannot completely replace the real 5G network and there will be some restrictions. Therefore, the second challenge is to design experiments in a way that allows the derivation of general statements about 5G security threats from observations made during the experiments

Contribution: This thesis will address the signaling attacks on the control plane of 5G networks by identifying concrete signaling scenarios to generate excessive packet floods, analyzing them, and demonstrating them to assess their impact on the network. The simulation environment will allow reproducing various attacks to derive characteristics of the attacks, which are required for detection by distinguishing between good and malicious communication patterns. Overall, this work will contribute to the improvement of network security.

The rapid growth of traffic and the number of connected devices in the 5G and beyond wireless networks focus the attention on sustainability in the radio access network (RAN). Traffic load and status of available wireless resources in the network change rapidly, especially in scenarios with a large number of connections and high mobility. The high connectivity is caused by exponentially increasing Internet of Things (IoT) devices connected to the network for supporting various use cases ranging from Industry 4.0 to healthcare.

IoT devices mainly powered by batteries are characterized by low cost, low complexity, and limited computational resources. Therefore, elongating their lifetime while fulfilling the quality of service (QoS) requirements poses a new research challenge. To tackle this problem, context awareness of devices consisting of device type and mobility; and the network traffic load simultaneously enhance the wireless resource management and the management of the devices states. Moreover, to enable awareness of the neighboring cells, the predicted information on the traffic load can be exchanged among cells. The above affects the decisions of accepting the device or offloading it to the neighboring cell and the device's operating state.

In this thesis, the student will focus on developing and testing a context-aware resource allocation mechanism based on device mobility and traffic load, focusing on decreasing individual devices' energy consumption and reducing processing latency.

• Good knowledge of Python and Matlab programming.
• Good mathematical background.
• Knowledge of mobile networks.

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.

Industrial IoT applications and systems are increasingly adopting service-oriented
architectures that leverage industrial IoT protocols such as DDS or MQTT. These protocols allow the specification of data flow qualities, also known as quality of service (QoS), on the application layer. QoS properties can include history, latency budgets, reliability, etc., and can be configured in the distributed middleware. However, there is a gap between the application layer QoS and the network layer QoS, especially in domains that use characteristic communication substrates such as 5G/6G. It is an open question how to combine both QoS sides to leverage an end-to-end guarantee. There is no direct mapping between the network QoS capabilities and the application layer QoS definitions, and no possibility to dynamically select an end-to-end QoS configuration for each specific topic. This thesis aims to evalute possibilities and concepts to bridge this gap to dynamically map application layer QoS to network layer QoS for industrial IoT applications over 5G/6G
network. s. The approach will consider requested quality services of the communication
flows and will leverage existing standards and interfaces such as 3GPP TS 123.501 and TS
123.502 for 5G network interacting to fulfill the requested QoS. The approach will be
evaluated in terms of feasibility and performance using simulations and/or testbeds.

Python, C++, Knowledge of Cellular Systems.

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

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

Industrial use cases require deterministic wireless connectivity. A promising base technology to fulfill these requirements is OFDMA-based Wi-Fi (i.e., Wi-Fi 6, Wi-Fi 7 and beyond) with advantages such as scalability, flexibility and mobility compared to wired networking solutions. However, current Wi-Fi mechanisms, as defined in the 802.11 standard, are not suited for industrial use cases due to their probabilistic channel access mechanisms and potential interference. Shaping future Wi-Fi solutions to cope with the demanding industrial requirements is an exciting
direction for upcoming research. Our previous work was based on deterministic channel access mechanisms that allow the AP to access the medium within an upper-bounded time and initiate an OFDMA transmission based on scheduled Trigger frames - thus, the problem of probabilistic channel access has been solved. To mitigate interference from neighbouring wireless devices, the IEEE drives standardization towards devices that can connect via multiple interfaces, i.e.,
Multi-Link Operation (MLO). One option using MLO is to duplicate packets on disjunct links [2] such that if a
transmission via one link is not successful, there is still a chance that the duplicated packet on the secondary link
is successfully transmitted, such that a retransmission of the packet can be avoided and latency can be reduced
(similar to the mechanisms defined in IEEE 802.1CB for Time-sensitive Networking).
The goal of this thesis is to investigate the different MLO mechanisms currently discussed in IEEE TGbe and
extend them towards reliable transmissions for industrial use cases. Two of the open points of MLO which could
be considered, are coordinated channel access of different links and the reduction of cross-interference with non
Simultaneous Transmit and Receive (NSTR) [1] Multi-link Devices (MLD). The core research question to answer
in this thesis is:

C++, Experience with NS3, Knowledge of CSMA and OFDMA is a plus.

The goal of this thesis is to evaluate weaknesses of the protocol implementation in commercial smart phones and design attacks correspondingly.

The goal of this thesis is to design and evaluate methods to localize mobile phones using UAVs.

Conventional network performance analysis approaches consist of theoretical analysis, simulation studies, and testbed measurements.

Theoretical approaches, e.g. queuing theory, impose strict assumptions on network conditions for modeling.

However, these assumptions generally do not hold in real networks and therefore lead to unrealistic performance benchmarks.

An alternative to the theoretical approach is computational modeling, which is also referred to as network simulation studies in the literature.

The network simulators are implemented in software to include a wide range of network elements and protocols.

The simulation approach is cheap and faster to implement in comparison to a real testbed setup.

However, it is computationally expensive and they cannot simulate complex scenarios in real-time.

The final method of conventional testbed measurements aims to build a small-scale prototype to measure network performance.

The limitation of this approach is the cost and complexity of building a setup consisting of real hardware.

An alternative to conventional network performance evaluation is machine learning (ML) based approaches.

Recent research on ML applications to network performance evaluation focuses on learning graph neural network (GNN) models for analysis.

GNN-based modeling allows the expression of network topologies as graphs and their generalization capabilities allow prediction for larger topologies that are unseen during the training phase.

Popular examples of this are RouteNet and xNet.

The objective of this thesis is to implement RouteNet and xNet models to establish their performance benchmarks and compare them. 

The first step is to build a packet-level simulator for data collection and establish the ground truth for GNN models.

The next steps are to implement the models and evaluate them.

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

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

Python.

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. However, these processing resources are usually shared among many applications, which potentially leads to worse performance in terms of execution time, throughput, etc. . This is especially critical for applications such as autonomous driving, telemedicine or smart operations. Hence, the processing of more critical applications must be prioritized.

In this thesis, the task is to develop and evaluate a priorization approach for applications. However, not only technical aspects will play a role for the priorization, but also ethical, i.e. in this case medical aspects. This is especially important, if applications are equally critical. For this, suitable real use cases are identified together with our partners at MITI (Hospital "Rechts der Isar"). The priorization approach then leads to a specified distribution of the processing and networking resources, satisfying the minimum needs of critical applications.

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

Motivation, basic networking knowledge, basic programming knowledge

In the previous generations of cellular networks, both data plane and control plane operations were conducted jointly in Radio Access Networks (RANs). With the emergence of Software Defined Networks (SDNs), and their adaptation in RANs, known as SD-RAN, for the first time the separation of control from data plane operations became possible in 5G RAN, as a paradigm shift on how the assignment of network resources is handled in particular, and how cellular networks operate in general. The control is shifted to centralized units, which are known as SD-RAN controllers. This brings considerable benefits into the cellular network because it detaches the monolithic RAN control and enables co-operation among different RAN components, i.e., Base Stations (BSs), improving this way network performance along multiple dimensions. Depending on the current spread of users (UEs) across BSs, and their channel conditions for which UEs periodically update their serving BSs, and BSs forward that information to the SD-RAN controller, the latter can reallocate resources to BSs accordingly. BSs then perform the resource allocation across their corresponding UEs. Consequently, exploiting the wide network knowledge leads to an overall improved performance as it allows for optimal allocation decisions.

This increased level of flexibility, which arises from having a broader view of the network, can be exploited in improving the mobility management in cellular networks. This comes into play even more with 6G networks in which in-network computing is envisioned to be integral part. Namely, users will be sending computationally-intensive tasks to edge clouds (through their BSs) and would be waiting some results as a response. However, as it will take some time until these tasks are run on the cloud, the user might be changing the serving BS. As a result, handover will have to be managed. However, while the task is being uploaded, performing handovers would not be good as then the task would need to be sent to another edge cloud. Consequently, having a centralized knowledge of all the network (which the SD-RAN controller has), to avoid frequent handovers, the controller has an extra degree of freedom by increasing the number of frequency blocks that can be assigned to a user while uploading the task and while downloading results.

In this thesis the goal would be to increase the overall network utility by deciding which tasks to serve (each task has its own utility), given the limited network resources in terms of the upload bandwidth, download bandwidth, storage in edge clouds, and finite computational capacity. Users besides sending tasks and receiving results are assumed to run other applications, with given service requirements. The student will formulate optimization problems and solve them either analytically or using an optimization solver, like Gurobi, CVX, etc. The other task would be to conductt realistic simulations and showing the advantages the developed algorithms offer against benchmarks.

Good knowledge of Python and interest to learn about mobility management in 5G

In pre-5G networks, both the data plane and control plane operations were performed jointly in Radio Access Networks (RANs). With the emergence of Software Defined Networks (SDNs), and its adaptation in RANs, known as SD-RAN, for the first time the separation of control from data plane became possible 5G RAN, as a paradigm shift on how the assignment of network resources is handled in particular, and how cellular networks operate in general. The control is transferred to centralized units, which are known as SD-RAN controllers. This brings considerable benefits into the mobile network since it detaches the monolithic RAN control and enables co-operation among different RAN components, i.e., Base Stations (BSs), improving the network performance along several dimensions. To that end, depending on the current spread of the users (UEs) across BSs, and their channel conditions for which the UEs periodically update their serving BSs, and BSs send that information to the SD-RAN controller, the latter can reallocate resources to BSs accordingly. BSs then perform the resource allocation across their corresponding UEs. As a consequence, exploiting the wide network knowledge leads to an overall improved performance as it allows for optimal allocation decisions.

This increased level of flexibility, which arises from having a broader view of the network, can be exploited in improving the mobility management in cellular networks. In the previous generations of cellular networks, each BS has its own set of frequencies at which it could transmit. Given that each user would receive service by only one BS, depending on the channel conditions the users would have with the serving BS and the number of users within the same sell, the user would decide on whether she would need a handover or it would be better to remain within the same serving area (i.e., receiving service from the same BS) . Currently, conditional handovers are being the most serious candidate for 5G. However, every handover involves a considerable cost, due to the preparations that need to be performed to hand a user over from one BS to another one. These will unavoidably lead to reductions in data rates and network resources for other users. On the other hand, having a centralized knowledge of all the network (which the SD-RAN controller has), to avoid frequent handovers, the controller has an extra degree of freedom by increasing the number of frequency blocks that can be assigned to a user experiencing bad channel conditions. This of course depends on the topology of the users in that moment.

In this thesis, the focus will be on jointly deciding on the resource allocation policy for each user across the entire area of the controller and when to perform the handover in order to optimize different performance aspects (e.g., provide proportional fairness). To that end, the student will formulate optimization problems and solve them either analytically or using an optimization solver, like Gurobi, CVX, etc. The other part would be conducting realistic simulations and showing the advantages the developed algorithms offer against state of the art.

Good knowledge of Python and interest to learn about mobility management in 5G

The goal of the thesis would be to build a Wireless Resource Allocation Framework to optimize Multi-hop, Multi-path networks.

The approach is to develop an optimization problem to allocate wireless and compute resources to users and to solve this problem using Reinforcement Learning.

• Strong Python programming skills
• Strong foundation on wireless communications
• Experience with Reinforcement Learning

Background

Network operators are confronting continuously increasing QoS (Quality of Service) requirements. The need for Internet bandwidth and low latency is getting more demanding. Thus, network operators must make appropriate network equipment upgrades to serve the user traffic. A german train IP-Optical network faces similar challenges. The network operators of a railway company must stand up to the challenge of the rising traffic requested from the passengers. They must develop a mechanism to quantify the nature of the new needed equipment (i.e. dimensioning) while leveraging the current advances in the Coherent Pluggable Transceivers (CPT) modules.

Problem Description

In the context of this thesis, you are called to model and solve the dimensioning problem for a train IP-Optical network using Integer Linear Programming (ILP). More specifically, the thesis consists of the following steps:

• literature research on RMSA (Routing, Modulation and Spectrum Assignment) and  CPT market
• adaptation of a given ILP model to the current scenario
• parametric study
• evaluation and visualization

Acquired Knowledge and Skills

In this thesis you will enrich your knowledge of IP-optical networks and ILP, a general methodology to find optimum solutions to linear problems. You will get an insight into core networking, network services, and modern challenges. Finally, you will learn how to run a parametric sweep simulation and evaluate the results.

Basic knowledge in:

• Communication Networks Architecture and Design
• Programming Experience
• Julia Language

Mobile Edge Computing (MEC) enabled 6G network support low latency applications running in energy-constrained and computational limited devices, especially IoT devices. Using the task offloading concept, the devices offload the incoming tasks fully or partially to MEC depending on the device and network side's communication and computation resource availability.

The 6G networks are oriented towards the Digital Twins (DT); therefore, the resource allocation and offloading decisions are enhanced with the context-awareness of the devices, environment, and network. The device context awareness consists of battery state, power consumption, CPU load, and traffic type. Further, the environmental context-awareness includes the position of the network components, the mobility patterns, and the predicted routes. Even though most IoT devices are accounted for as static devices, there are use cases in NR-Light where devices obtain mobility.

In this thesis, the student will focus on developing and testing a context-aware communication and computing resource allocation mechanism, focusing on decreasing individual devices' energy consumption and reducing processing latency.

• Good knowledge of Python and Matlab programming.
• Good mathematical background.
• Knowledge of mobile networks.

Network Intrusion Detection (NID) is a common topic in cybersecurity. However, it is not trivial to find a solution when facing the complicated network environment nowadays. Often a complex system is needed to process enormous volume of data stored in databases. This thesis proposes to use Deep Learning models to tackle the NID problem in a pre-train/fine-tune manner. As the new paradigm of transfer learning, the process of pre-training follows by fine-tuning has achieved huge success in many areas such as vision and NLP. We aim to study whether those trending models still perform well on large-scale structured data such as network security logs. It is plausible to leverage the strong learning ability of DL models to learn table representations and separate anomaly from benign records based on the learned information.

• Machine learning knowdlege
• Programming skills (Python, GIT)
• Computer networking knowledge

Today's Data Center (DC) networks are facing increasing demands and a plethora of requirements. Factors for this are the rise of Cloud Computing, Virtualization and emerging high data rate applications such as distributed Machine Learning frameworks.
Many proposal for network designs and routing algorithms covering different operational goals and requirements have been proposed.
This variety makes it hard for operators to choose the ``right'' solution.
Recently, some works proposed that automatically generate adversarial input to networks or networking algorithms [1,2] to identify weak spots in order to get a better view of their performance and help operators' decision making. However, they focus on specific scenarios.
The goal of this thesis is to develop or extend such mechanisms so that they can be applied a wider range of scenarios than previously.
The thesis builds upon an existing flow-level simulator in C++ and initial algorithms that generate adversarial inputs for networking problems.

[1] S. Lettner and A. Blenk, “Adversarial Network Algorithm Benchmarking,” in Proceedings of the 15th International Conference on emerging Networking EXperiments and Technologies, Orlando FL USA, Dec. 2019, pp. 31–33, doi: 10.1145/3360468.3366779.
[2] J. Zerwas et al., “NetBOA: Self-Driving Network Benchmarking,” in Proceedings of the 2019 Workshop on Network Meets AI & ML  - NetAI’19, Beijing, China, 2019, pp. 8–14, doi: 10.1145/3341216.3342207.

- Profound knowledge in C++

Bayerischer Rundfunk (BR) operates a network to deliver content via television, radio and the internet to its users. This requires a highly heterogenous network. The network monitoring solution for the BR-network collects log data from involved devices and stores it in a central database. Currently, human operators make network management decisions based on a manual review of this log data. This especially includes root cause identification in case of network failures. Such a human-centric process can be tedious and does not scale well with increasing network complexity. In this thesis, the student should perform a thourough analysis of the described data and evaluate the potential for automated processing. Goal is to provide a data-driven approach that significantly supports human operators with identifying root causes in case of network failures.

Software Defined Networking (SDN) separates the control and data planes.Control plane can be considered as the brain of the network and it is responsible for configuring flows, finding paths and managing all the network functionalities like firewall,  load balancing,  etc.  For this reason,  the SDN controller became complex.  Furthermore, it is a large software platform, which have many contributors  with  different  experience  level.   As  a  result  the  code  contains  many undetected and unresolved bugs.  If one of these bugs is activated in the operational state, it may cause performance degradation or even collapse of the whole system.

SDN serves to broad range of applications with different requirements.  Some of the application areas like autonomous driving requires high reliability and performance degradation may cause undesired results.  Software Reliability Growth Models (SRGM) are statistical frameworks that are based on historical bug reports  for  reliability  analysis  and  widely  used  to  estimate  the  reliability  of  a software.  Open network operating system (ONOS) is an open source project and it became one of the most popular SDN platforms.  Its historical bug reports are open in their JIRA issue tracker.  Currently ONOS has 23 releases, its first ten  versions  are  investigated  with  different  SRGM  models  [1]  and  found  that different SRGMs fit to the bug detection of different versions of ONOS.

Source code metrics refer to quantitative characteristics of the code.  Those metrics  can  describe  the  size  of  the  code  (lines  of  code),  complexity  of  code (McCabe’s complexity), etc.  They have been used to predicting the number of bugs, identifying possible potential location of bug, etc.

The goal of this work is to analyse the reliability of different ONOS releases. For that purpose, an understanding of the correlation between the structure of source code and the bug manifestation process is crucial to predict the future bug manifestation of the new releases.  First, a state of the art research on the SRGM will  be  done  to  understand  the  software  reliability  and  SRGMs.   Afterwards the  student  should  implement  different  SRGMs  to  fit  the  error  manifestation of  every  release  and  compare  the  results  with  mentioned  research  [1].   Then, different  code  metrics  will  be  obtained  from  each  ONOS  release.   Then,  the correlation between SRGM and code metrics will be revealed.  At last reliability of the release will be analyzed with the best fitting SRGM. The result of this work will be to propose a reliability metric combining SRGM and code metrics that improves the software reliability prediction.

References

P. Vizarreta, K. Trivedi, B. Helvik, P. Heegaard, W. Kellerer, and C. Mas Machuca, An empirical study of software reliability in SDN controllers,  13th International  Conference  on  Network  and  Service  Management  (CNSM), 2017.

In the Radio Access Network (RAN), the MAC scheduler is largely inherited across generations in the past, to fit to new networking goals and service requirements. The rapid deployment of new 5G technologies will make upgrading of current ones extremely complicated and difficult to improve and maintain. Therefore, finding new solutions for efficient Radio Resource Scheduling (RRS) is necessary to meet the new KPI targets. 5G networks and beyond use the concept of network slicing by forging virtual instances (slices) of its physical infrastructure. A heterogeneous network requires a more optimized and dynamic RRS approach. In view of the development of SD-RAN controllers and artificial intelligence, new promising tools such as reinforcement learning can be proven useful for such a problem.

In this thesis, a data-driven MAC slice scheduler will be implemented, that maximizes user utility, while learning the optimal slice partitioning ratio. A deep reinforcement learning technique will be used to evaluate the radio resource scheduling and slicing in RAN. The results will be compared with traditional schedulers from the state-of-the-art.

...

In this thesis student will focus on designing and implementing a hierarchical SDN solution for industrial multi-domain TSN network.

Recently, a scalable load balancing algorithm in the dataplane has been proposed that leverages P4 to estimate the utilization in the network and assign flows to the least utilized path. This approach can be interpreted as a form of Graham's List algorithm.

In this thesis, the student is tasked to investigate how a different online scheduling algorithm called IMBAL performs compared to HULA. A prototype of IMBAL should be implemented in NS3. The tasks of this thesis are:

1. Literature research and overview to online scheduling and traffic engineering in data center networks.
2. Design how IMBAL can be implemented in NS3.
3. Implementation of IMBAL in NS3.
4. Evaluation of the implementation in NS3 with production traffic traces and comparison to HULA (a HULA implementation is provided from the chair and its implementation not part of this thesis).

Next generation mobile networks are envisioned to cope with heterogeneous applications with diverse requirements. To this end, 5G is paving the way towards more scalable and higher performing deployments. This leads to a revised architecture, where the majority of the functionalities are implemented as network functions, which could be scaled up/down depending on the application requirements. 

3GPP has already released the 5G architecture overview, however there exists no actual open source deployment of RAN functionalities. This will be crucial towards the evaluation of the Core network both in terms of scalability and performance. In this thesis, the student shall understand the 5G standardization, especially the control plane communication between the RAN and 5G Core. Further, an initial RAN function compatible with the 5G standards shall be implemented and evaluation of control plane performance will be carried out. 

• Strong knowledge on programming languages Python, C++ or Java.
• Knowledge about mobile networking is necessary.
• Knowlegde about 4G/5G architecture is a plus.

In order to fully utilize the capabilities of a LiFi-RF Heterogeneous network, the client devices should be capable of using multiple network interfaces simultaneously. Thanks to multipath solutions like MPTCP, this is possible. 

The challenge in a MPTCP-enabled heterogeneous network lies in designing a policy to schedule data packets onto the multiple paths with heterogeneous characteristics (eg. delay, packet loss).

This work involves

• Designing an MPTCP scheduler that schedules packets optimally to minimize network delay and handle the dynamicity of heterogeneous links
• Implementing the scheduler in the Linux kernel
• Performing extensive evaluations with Mininet and hardware

Related Reading:Yang, Wenjun, et al. "Loss-aware throughput estimation scheduler for multi-path TCP in heterogeneous wireless networks." IEEE Transactions on Wireless Communications 20.5 (2021): 3336-3349.

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

• Strong Python and C++ programming skills
• Experience with optimization problems
• Experience with Linux networking

Mobile Edge Computing (MEC) enabled 6G network support low latency applications running in energy-constrained and computational limited devices, especially IoT devices. Using the task offloading concept, the devices offload the incoming tasks fully or partially to MEC depending on the device and network side's communication and computation resource availability.

The 6G networks are oriented towards the Digital Twins (DT); therefore, the resource allocation and offloading decisions are enhanced with the context-awareness of the devices, environment, and network. The device context awareness consists of battery state, power consumption, CPU load, and traffic type. Further, the environmental context-awareness includes the position of the network components, the mobility patterns, and the quality of the wireless channel and the availability of the network resources.

In this project, the student will focus on developing and testing an artificial intelligence algorithm for joint allocating of computing and radio resources in a predictive manner, focusing on decreasing individual devices' energy consumption and reducing processing latency.

Tasks

• Work with a 6G radio access network simulator, to generate the database for the scenario with devices having high energy efficiency and low task processing latency requirements.
• Develop a reinforcement learning algorithm for joint allocation of radio and computing resource allocation.
• Comparing the developed model with the state-of-the-art approaches.
• Test and documentation.

• Good knowledge of Python programming.
• Good mathematical background.
• Good knowledge of deep learning/reinforcement learning.

 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.

Modern clusters provide many knobs that cloud administrators can tune to configure their system. However, different configurations lead to different levels of performance. In order to find an optimized configuration and to try out different "what-if" scenarios, Digital Twins (DT) have been suggested as a possible solution. A Digital Twin is a virtual model of a physical object or system, that describes the behavior of the real peer with high accuracy. DTs leverage historical and real-time data in order to keep up with the real system.

One of the challenge of creating an accurate DT of a cluster is accurately modeling and predicting the demand.  Because of the high variability of the network traffic, this task has proven challenging in the past, but recent works, such as DoppelGANger [1], enable a modeling of the network traffic volume that better matches reality.

DoppelGANger creates high fidelity synthetic datasets modeled from real traces by leveraging Generative Adversarial Networks (GANs). Moreover, it is able to capture the underlying long-term dependencies and complex multidimensional relationships, surpassing other GAN and non-GAN-based approaches.

Concretely, DoppelGANger has shown improved performance over the state-of-the-art on three different datasets. In this student thesis, we want to explore how DoppelGANger performs also on three other datasets (Fifa '95 World Cup, Bibsonomy, NASA) and discover possible limitations of it. Additionally, we want to analyze the suitability of using DoppelGANger in a real-time manner.

[1] Zinan Lin, Alankar Jain, Chen Wang, Giulia Fanti, and Vyas Sekar. 2020. Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions. In Proceedings of the ACM Internet Measurement Conference (IMC '20). Association for Computing Machinery, New York, NY, USA, 464–483. https://doi.org/10.1145/3419394.3423643

• Proficiency in Python
• Interest in data-driven modeling and time-series analysis.
• Understanding of Machine Learning (ML) concepts and models
• Experience with the NumPy and Pandas libraries

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.

- Good C/C++ experience

- Good Matlab knowledge

 - Medium knowledge on OFDM and Wireless Channel Estimation

- Good Python knowledge is a plus

- Machine Learning understanding is a plus

As a new and future mobile communications standard, 6G is intended to focus on people. In addition to very low latencies and high data transmission rates, the network will be more reliable, secure and dynamic. These features are particularly in demand in medicine and medical device technology to develop intelligent value-added functions for patients and clinical staff. As part of the BMBF-funded joint project "6G-life", a demonstrator will be developed that uses and requires these network properties to enable new intelligent assistance functions.

The aim of this research practice is the further development of a system for the environment-aware measurement of vital parameters. For this purpose, a detailed literature research on the possibilities of biosignal processing and data transmission protocols in the medical field will be conducted. Subsequently, an already existing measurement setup is to be further developed, which records vital parameters by means of suitable sensors (ECG, temperature, SpO2, blood pressure, ...). A message architecture is to be designed that allows secure and controllable transmission of the sensor data to computing units located in the network for biosignal processing. The results of the signal processing are to be displayed on an already existing GUI.

This Research Internship is in cooperation with the MITI research group. Most work may be done at their site.

• Motivation and independent working style
• Knowledge in communication networks and signal processing
• Knowledge in Python
• Knowledge in C++ if possible

Digital Twin is a comprehensive digital model of an environment,
product, or system used for testing, integration, and simulation without impacting its real-world counterpart. The 5G Digital Twin is an approach to testing and assurance providing an emulated, software replica of the 5G physical network that allows for continuous prototyping, testing, assuring, and self-optimization of the living network. It simulates a 5G base station with functionality that includes data transfers, mobility control, session management, radio access network sharing, positioning, and functional splits. It also enables testing on demand, without
interruption, where any aspect of the physical operation system can be examined for any possible situation i.e., traffic congestion, security breaches, or equivalent going down. As a result, developing new use cases virtually will be accomplished for less cost in time and money. Digital Twin can identify any process failures before the product goes into production and also provide real-time remote monitoring by enabling users to be able to monitor and control the system performance remotely and access it anywhere.

5G NR with TDD defines a maximum allowed offset between adjacent gNBs to be less than 2 us as defined by ITU. However, what happens exactly to the throughput and latency when the time is offset is still to be found. This internship plans to evaluate this gap in the state of the art.

We will implement agressor models in MATLAB for the evaluation.

MATLAB, 5G NR

ROS is an open-source, meta-operating system for building robot applications. It provides the services to develop a complex system using existing solutions for small problems. It also provides services like hardware abstraction, message-passing between processes and package management.
The Robot Operating System (ROS) is a framework and set of tools and libraries that provide functionality across multiple computers. The key feature of ROS is that it allows you to design complex software without knowing for certain how does the hardware work. The way that the software runs and communicates, makes it very easy for users. ROS is a very important and necessary topic because it provides functionality for device drivers, hardware abstraction, tools for
testing and visualization and communication between processes over multiple machines. It connects a network of processes(nodes), which run on multiple devices, with a central hub.

Every Operator wants to get the best performance out of their network. Real-time Net-
works are no exception.In this thesis, we consider networks that already exist.Instead
of just evaluating their performance, we want to evaluate whether small changes in the
topology - adding a few links - can change the performance of the network. To be able to
do this, the operator needs full control of the network.Therefore, we consider real-time
networks where this is the case.
The goals of this research internship:
•Include a way of adding links to the topologies
•Find already known heuristics (e.g. from reliability)
•Create a new way of adding links that includes real-time network-specific knowledge,
i.e., the delay on links
Possible Evaluations:
•Evaluate the performance change based on Network Utilization, Current Link Delay,
and Number of Admissions.
•Evaluate performance based on the number links added
•Compared different ways of adding links

Background

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 motivates the exploration of networks enabling train control and on-board data communications under mobility scenarios with high reliability and controlled latency.

Research question

The goal of this work is to analyze the robustness of the Rail Data Network and its variants to identify the most robust alternative. The topologies and traffic matrix will be given. The plan is to develop robustness surfaces [1] to understand the strengths and weaknesses of different networks combinations.

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.

 References

[1] Manzano, M., Sahneh, F., Scoglio, C., Calle, E. and Marzo, J.L., 2014. Robustness surfaces of complex networks. Scientific reports, 4(1), p.6133.

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

[3] 5G-Rail FRMCS. Last visit on 13.12.2021 https://5grail.eu/frmcs/

Requirements

Basic knowledge in:

• Python
• Communication Network Reliability course at LKN or equivalent knowledge.

Please send your CV and transcript of records.

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/

Basic knowledge in:

• Integer Linear Programming (ILP), heuristics or Machine Learning (ML).

• Python

Please send your CV and transcript of records.

The goal of the thesis would be to build an Anticipatory or Proactive Wireless Resource Allocation Framework to optimize Multi-path networks.

The approach is to develop an optimization problem to allocate network resources to users by looking into window of time in the future and to solve this problem using Reinforcement Learning.

By knowing the channel quality of the users in the future, a better, more optimal allocation of resources is made possible.

Related Reading:

Chen, Weixi, et al. "Proactive 3C Resource Allocation for Wireless Virtual Reality Using Deep Reinforcement Learning." 2021 IEEE Global Communications Conference (GLOBECOM). IEEE, 2021

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.

• Strong Python programming skills
• Strong foundation on wireless communications
• Experience with Reinforcement Learning

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/

Basic knowledge in:

• Integer Linear Programming (ILP), heuristics or Machine Learning (ML).

• Python

Please send your CV and transcript of records.

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Energy efficiency is one of the key performance requirements in the 5G network to ensure user experience. A portion of devices, especially the Industrial Internet of Things (IIoT), run on limited energy, supported by the batteries not placed over the lifetime.

Therefore, the estimation of the power consumption and battery lifetime has recently received increased attention. Multiple context parameters, such as mobility and traffic arrivals, impact the device's power consumption.

In this thesis, the student shall focus on analysing the impact of different traffic models on the power consumption of user equipment. Different source and aggregated traffic models will be implemented depending on the number of devices n the scenario. The implemented traffic models will be evaluated based on a context-aware power consumption model for the user equipment.

• Good knowledge of Python and Matlab programming.
• Good mathematical background.
• Knowledge mobile networks.

Increased interference is one of the main drawbacks of cell densification, which is an important strategy for 5G networks to achieve higher data rates. Function centralization has been proposed as a strategy to counter this problem, by letting the physical or scheduling functions coordinate among one another. Nevertheless, the capacity of the fronthaul network limits the feasibility of this strategy, as the throughput required to connect low level functions is very high. Fortunately, since not every function benefits in the same way from centralization, a more flexible approach can be used. Instead of centralizing all functions, only those providing the highest amount of interference mitigation can be centralized. In addition, the centralization level, or functional split, can be change during runtime according to the instantaneous network conditions. Nonetheless, it is not fully know how costly it is to deploy and operate a network implementing a dynamic functional split.

In this internship, the cost of a radio access network implementing a dynamic functional split will be evaluated. A simulator already developed at LKN will be used and extended to produce network configurations adapted to the instantaneous user position and activity. Then, off-the-shelf cost models will be improved and used to estimate the deployment and operating cost of the network under multiple scenarios. Furthermore, the conditions on which a dynamic functional split is profitable will be investigated. Improvements on the functional-split selection algorithm will be proposed, such that the operator benefits from enhanced performance without operating at exceedingly costly states. Finally, a model that takes into account the cost of finding and implementing a new functional split will be employed and its results compared to the previous results.

...

In electronics and telecommunication, jitter is a significant and an undesired factor. The effect of jitter on the signal depends on the nature of the jitter. It is important to sample jitter and noise sources when the clock frequency is especially prone to jitter or when one is debugging failure sources in the transmission of high speed serial signals. Managing jitter is of utmost importance and the methods of jitter decomposition have changed comparably over the past years.

In a system, jitter has many contributions and it is not an easy job to identify the contributors. It is difficult to get Random Jitter on a spectrogram. The waveforms are initially constant, but the 1/f noise and flicker noise cause a lot of disturbance when it comes to output measurement at particular frequencies in a system. 

The task is to understand the difference between the jitter calculations based on a step response estimation and the dual dirac model by comparing the jitter algorithms between the R&S oscilloscope and other competition oscilloscopes. Also to understand how well the jitter decomposition and identification is there.

The tasks in detail are as follows.

Setup a waveform simulation environment and extend to elaborate test cases

Run the generated waveforms through the algorithms

Analyze and compare the results:

Frequency domain

Statistically (histogram, etc)

Time domain

Consistency of the results

Evaluate the estimation of the BER (bit error rate)

Identify the limitations of the dual-dirac model

Compare dual-dirac model results with a calculation based on the step response estimation

Generate new waveforms based on the analysis

Summarize findings

The goal of this thesis is the classification of packet-level traces using Markov- and Hidden Markov Model. The scenario is open-world: Traffic of specific web applications should be distinguished from all possible web-pages (background traffic). In addition, several pages should be differentiated. Examples include: Google Maps, Youtube, Google Search, Facebook, Google Drive, Instagram, Amazon Store, Amazon Prime Video, etc.

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The architecture of 5G radio access networks features the division of the base station (gNodeB) into a centralized unit (CU) and a distributed unit (DU). This division enables cost reduction and better user experience via enhanced interference mitigation. Recent research proposes the posibility to modify this functional split dynamically, that is, to lively change the functions that run on the CU and DU. This has interesting implications at the network operation.

In this topic, the student will employ a dedicated simulator developed by LKN to characterize the duration and transition rates of each functional split under multiple variables: population density, mitigation capabilities, mobility, etc. This characterization may be used then on traffic models to predict the network behavior.

MATLAB, some experience with mobile networks and simulators

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.

Most important:

• Motivation and willingness to learn unknown things.
• Ability to work with various partners (teamwork ability).

Of advantage:

• C/C++ and knowledge about how other programming languages work (Python, etc.)
• Knowledge about communication networks (exspecially the RAN), 5G concepts, the P4 language, SDN, Linux.
• Initiative to bring in own ideas and solutions.

Please note: It is not necessary to know about every topic aforementioned, much more it is important to be willing to read oneself in.

The main responsibilities of a working student include assistance to tutors in the correction of the programming assignments, as well as answering the questions in Moodle.  Working time is 6-7 hours per week in the period from May to July.

• Python knowledge

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

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

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

Communication Networks must fulfill a strict set of requirements in the Industrial Area. The Networks must fulfill strict latency and bandwidth requirements to allow trouble-free operation. Typically, the industry relies on purpose build solutions that can satisfy the requirements.

Recently, the industry is moving towards using Ethernet-based Networks for their use case. This enables us to use common of the shelf hardware to communicate within the network. However, this hardware still will execute industrial applications and therefore has the same strict requirements as the network. In this project, we consider Linux-based hosts that run the industrial applications. We consider different networking hardware and configurations of the system to see how it affects performance. The goal is to investigate the overhead of the host. 

Your tasks within the project are :

• Measure the Host Latency with different NICs
• Measure the Host Latency with different Hardware Offloads
• Tune, configure, and measure the Linux Scheduler to improve performance

You will gain:

• Experience with Networking Hardware
• Experience with Hardware Measurements 
• Experience with Test Automation

Please send a short intro of yourself with your CV and transcript of records to us. We are looking forward to meeting you.

• Familiarity with Linux Console
• Python
• C (not required, but a plus)

Availability is the probability that a device performs its required function at a particular instant of time.

In most networks, the components are brought from different manufacturers. They have different availabilities. Network operators prefer having reliable components handling more traffic. This ensures the robustness of the network. So, assigning appropriate manufacturers to the components in the topology to guarantee maximum availability is essential.

In this work, the student uses centrality metrics to identify the critical nodes and assign manufacturers based on these metrics.

Mandatory:

• Kommunikationsnetze course at LKN
• Python

Availability is the probability that a device performs its required function at a particular instant of time.

In most networks, the components are brought from different manufacturers. They have different availabilities. Network operators prefer having reliable components handling more traffic. This ensures the robustness of the network. So, assigning appropriate manufacturers to the components in the topology guaranteeing 
a) maximum availability, and 
b) load balancing on the nodes
is essential.

For a fixed topology and known traffic, how can the components be assigned to manufacturers to maximise availability and balance load on nodes?

Mandatory:

• Communication Network Reliability course/ Optical Networks course at LKN
• Python

Preferred:

• Knowledge of Linear Programming and/or nonlinear programming

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.

• Most important: Motivation and willingness to learn unknown things.
• C/C++ and knowledge about how other programming languages work (Python, etc.) and/or the willingness to work oneself into such languages.
• Preferred: Knowledge about communication networks (exspecially the RAN), 5G concepts, the P4 language, SDN, Linux.
• Initiative to bring in own ideas and solutions.
• Ability to work with various partners (teamwork ability).

Please note: It is not necessary to know about every topic aforementioned, much more it is important to be willing to read oneself in.

 Software-Defined Networking (SDN) is a network paradigm where control and data planes are decoupled. The control plane consists on a controller, which manages network functionality and can be deployed in one or multiple servers. The data plane consists on forwarding entities which are instructed by the controller on how to forward traffic.

 A network can be divided in multiple domains in order to ease its management or limit ownership. In multi-domain SDN, each domain has a controller which is responsible for the management. Controllers in different domains cooperate which each other aiming at providing multi-domain end-to-end connectivity.

 In this work, the student will receive an abstract topology representing the multi-domain network. This information has to be used to build a virtual network, that can be used in the testing of different algorithms. The implementation should include a GUI, in order to visualize the topology and interact with the different elements in the network.

Please send your CV and transcript of records.

Basic knowledge on the following:

• Linux
• Networking/SDN
• Python
• Web programming (GUI)

As preliminary results show, Linux TCP/IP Networking Stack introduces a high networking delay. The topic of this work is to perform an empirical study on the Linux socket-based transmission approach and implement a delay measurement workflow based on existing foundations and repositories.

References:

[1] Where has my time gone?

Basic knowledge of 

• Networking and Linux
• C

Future IoT will need wireless links with high data rates, low latency and reliable connectivity despite the limited radio spectrum. Connected lighting is an interesting infrastructure for IoT services because it enables visible light communication (VLC), i.e. a wireless communication using unlicensed light spectrum. This work will aim at developing a tool to perform an economic evaluation of the proposed solution in the particular case of a smart office.

For that purpose, the following tasks will have to be performed:

• Definition of a high-level framework specifying the different modules that will be implemented as well as the required inputs and the expected outputs of the tool.
• Development of a cost evaluation Excel-VBA tool. This tool will allow to evaluate different variations of the selected case study and if possible, to compare different alternative models (e.g., dimensioning) or scenarios (e.g., building types).

- Excel and VBA

Network Function Virtualization (NFV) is becoming a promissing technology in modern networks. A challenging problem is determining the placement of Virtual Network Functions (VNFs). In this work, we plan to implement existing algorithms for embedding VNFs chains in NFV-enabled networks. 

Experience in Python or Java, object oriented programming

The lecture Broadband Communication Networks (Prof. Wolfgang Kellerer) teaches network-related methods of mobile communication: WIFI, 2G to 5G cellular networks, etc. In order to bridge the gap between the methods and real life, innovative teaching concepts shall be developed in form of short podcasts where the students learn in short episodes about wireless communication and networking in day to day scenarios. In the podcasts the student should also be introduced to short exercises they can perform on their own.

In order to support designing these podcasts Pro. Kellerer is looking for a student experienced in programming and maybe in podcasts to help him.

Very good programming skills; experience with podcasts; experience with app programming (on android/ios)