Student Work

Age of Information(AoI) is defined as the elapsed time since the generation of the most recent packet. It has been used as a cross-layer metric in remote monitoring and controlling scenarios.

AoI-aware networking & protocol design requires the modification within the communication stack. To that end, our department has set up a testbed that consists of multiple software-defined radios (SDRs) programmed in GNU Radio. This enables us to develop our own solutions, e.g., MAC protocol, routing algorithms etc. to improve e.g. AoI performance at the application layer.

This tasks of this project will consist of:

• Working with the existing setup, that consists of multiple SDRs programmed with GNU Radio / C++
• Scheduling of flows over multi-hop paths between the source and the destination pairs
• Performance analysis and comparison of experimental results to analytical ones from the literature

The project requires:

• Solid C/C++ knowledge and low-level programming
• Python skills are beneficial
• Solid fundamental knowledge in wireless communications
• Having worked with practical implementations of wireless communication devices is a great benefit, such as SDRs or sensor motes (some examples are Telosb Motes, Zolertia Re-motes, ...)

The 5G cellular networks are the state-of-the-art cellular networks for the coming 10 years. One of the critical network functions in the 5G core system is the evolved packet gateway or User Plane Function (UPF). The UPF is responsible for carrying the users' packets from the base stations to the data network (like the internet). 

On the other hand, P4 is a promising language for programming packet processors. It can be used to program different networking devices (Software/FPGAs/ASICs/...).

Using P4 to implement the UPF has many advantages in terms of flexibility and scalability. In this work, the student will realize/implement the UPF in P4 language. Then, the advantages of this approach, especially in terms of performance gains, will be evaluated.

-Good programming skills

-Basic Linux knowledge

-Motivation and critical thinking

-Knowledge about LTE, 5G, or P4 is a plus.

As the architecture of the 5G Core has already been standardized and it is fundamentally different from the previous generations, a question rises regarding the efficiency of the communication mechanisms that will be used for the communication between Network Functions (NFs) inside the 5G Core Network.
In this project, the student will evaluate these mechanisms used in an open-source implementation of the 5G Core (https://www.free5gc.org/), and propose improvements that will increase the performance and scalability of the Core Network (e.g., serialization, protocols, etc.).

• General knowledge about Mobile Networks (RAN & Core).
• Motivated to learn more about the communication side of the Mobile Core Networks.
• Strong programming skills. Knowledge of Go (https://golang.org/) is a plus.

endri.goshi@tum.de

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.

endri.goshi@tum.de

LiFi is becoming an increasingly important technology as the unlicensed ISM band gets overcrowded. LiFi, with its high data rates, emerges as a promising solution to provide connectivity using LED lights thereby reducing the interference with other wireless technologies. The integration of LiFi into existing communication systems is best envisioned in the form of wireless Heterogeneous Networks or HetNets where the short-range, additional capacity providing LiFi cells complement the broader coverage providing WiFi cells. Any user device in such a HetNet is equipped with multiple wireless interfaces i.e. LiFi and WiFi.

The challenge in a such a heterogeneous network is in the wireless resource allocation. Most of the existing research focuses on this problem in a scenario where the client is either connected to LiFi or WiFi. But, in order to fully utilize the potential of a inter-technology interference free LiFi-WiFi HetNet, the scenario where the client is simultaneously served by LiFi and WiFi must be considered.

In this work, the student is expected to formulate and solve an optimization problem to allocate wireless resources while maximizing the network sum throughput in a LiFi-RF dual connectivity scenario. The scenario should then be simulated and results compared with existing literature.

• Python
• Sound knowledge of wireless communication
• LiFi knowledege is an advantage but not necessary
• Experience with optimization problems is an advantage

Age of Information is a metric measuring information freshness at the receiver in a remote monitoring scenario. (For details, please see: https://core.ac.uk/download/pdf/189662885.pdf) It has been used as a performance metric in communications to evaluate various medium access schemes, such as Carrier Sense Multiple Access (CSMA).

This project requires a practical implementation of CSMA and similar conventional contention-based MAC algorithms on Software Defined Radios (SDRs). The student is asked to implement CSMA algorithm with GNUradio and test on a practical setup consisting of multiple SDRs. The implementation can be done in Python or C++ based on the preference of the student.

Note: The content will be extended further in case of a Master's thesis.

The student is asked to have:

• Solid knowledge in wireless communications
• Advanced programming skills in Python or C++ programming languages
• Some experience with practical hardware in the past, e.g. wireless sensor motes, SDRs.
• Good communication & writing skills in English
• Motivation and self-organization throughout the thesis

It is beneficial to have GNUradio experience but not mandatory.

IEEE 802.1 TSN standards aim to provide for mechanisms and methods for deterministic delay and jitter in industrial networks. Sources of delay and jitter in a TSN are numerous, i.e., misconfigured or incorrectly dimensioned TAS schedules, congested egress ports, large clock jitter in distributed time synchronization, congestion at egress / ingress ports of the end-stations are all possible causes of poor end-to-end performance.

In this work, an existing approach to end-to-end monitoring of transmission delay and jitter will be extended to support for quick identification of source of the anomaly in a TSN scenario (i.e., the root cause). Two or more approaches will be proposed to cater for end-to-end monitoring of TSN streams, relying on:

1) intermediate, pre-placed network TAPs;

2) hardware / software data plane agents capable of telemetry and dynamic re-programming to support the quick identification of anomaly sources for individual streams.

Any resulting overhead of the intermediary monitoring solution will be quantified for consideration in the TSN stream planning. The work will hence implement and validate the monitoring mechanisms in an existing TSN testbed that relies on a Linux-based control plane, and will optionally involve P4-based middlebox devices to serve as intermediate monitoring entities. 

[1] 802.1Qbv - Enhancements for Scheduled Traffic - http://www.ieee802.org/1/pages/802.1bv.html

[2] Improving Network Monitoring and Management with Programmable Data Planes - https://p4.org/p4/inband-network-telemetry/

- Very good knowledge of networking concepts and architectures

- Very good knowledge and practical experiences with Linux networking

- Good knowledge of Python, C, C++ or Rust

- Any prior experience with TSN is a plus

- Any prior experience with P4/SDN is a plus

- Proficient English and/or German communication skills

- Ability to work and pursue solutions under limited supervision

ermin.sakic@siemens.com

blenk@tum.de

andreas.zirkler@siemens.com

Industry 4.0 scenarios have introduced the novel requirements for dynamic device-to-device interaction without strict plan-ahead interactions (typical of Industrial Ethernet of the last two decades). For example, the use cases of work piece de-attachment and interacting Automated Guided Vehicles (AGVs) in factory automation impose the need for dynamic inter-connection of distributed industrial applications at runtime [1] without a plan-ahead schedule.

Dynamic TSN stream establishment has been proposed by IEEE802.1 to address scenarios of applications requesting real-time communication services at runtime [2, 3]. TSN schedulers capable of computing the transmission schedules with guaranteed real-time properties (upper-bound constraints on latency, jitter, and guaranteed bandwidth) have accordingly been proposed in the past, however, they have traditionally focused on one-time / offline execution [4].

Combined with the requirement for dynamic and efficient resource (de-)allocation, transmission schedules must generally be adaptable at runtime in order to provide for maximum efficiency in network resource utilization. This work will investigate the runtime adaptation of TSN control and data plane to support for the dynamic stream allocation. Algorithms for adaptation of existing TAS-based schedules with minimal impact on availability (packet loss) and QoS guarantees for running TSN streams will be proposed, implemented, and validated. Novel metrics for stream consistency conservation may need to be proposed by the approach. The resulting approach will eventually be evaluated in NeSTiNg (OMNeT++/INET) simulator and/or a small-scale physical testbed comprising multiple TSN switches and end-stations.

[1] IEC/IEEE 60802 TSN Profile for Industrial Automation - https://1.ieee802.org/tsn/iec-ieee-60802/

[2] P802.1Qdd – Resource Allocation Protocol - https://1.ieee802.org/tsn/802-1qdd/

[3] 802.1Qat - Stream Reservation Protocol -/ http://www.ieee802.org/1/pages/802.1at.html

[4] Dürr et al., No-wait Packet Scheduling for IEEE Time-sensitive Networks (TSN) - https://dl.acm.org/doi/abs/10.1145/2997465.2997494

- Very good knowledge of networking concepts and architectures

- Very good knowledge and practical experiences with Linux networking

- Good knowledge of Python, C, C++ or Rust

- Any prior experience with optimization methods and and tools and OMNeT++  is a plus

- Any prior experience with TSN is a plus

- Any prior experience with SDN is a plus

- Proficient English and/or German communication skills

- Ability to work and pursue solutions under limited supervision

ermin.sakic@siemens.com

blenk@tum.de

andreas.zirkler@siemens.com

IEEE 802.1Qbv Time-Aware Shaper (TAS) standardizes the mechanisms for TDMA-based packet transmission in future Ethernet deployments, as well as the interfaces for definition and configuration of associated transmission schedules [1].

In this work, a state-of-the-art end-to-end TAS-based scheduler will be extended with schemes enabling for data plane redundancy under consideration of worst-case delay and jitter constraints. Support for TSN streams with 1:1 replication for proactive redundancy, as well as an approach to efficient schedule reservation with reactive 'fast-failover' redundancy will be investigated, implemented, and subsequently evaluated in terms of adherence to jitter, delay, and packet loss constraints, as well as the computation complexity and acceptance rates of the approach.

The joint routing/scheduling task will first be formulated as an optimization problem and implemented using an optimization solver. The student will then extend the existing TAS scheduler relying on metaheuristics for faster execution compared to using a solver.

[1] IEEE 802.1Qbv - Enhancements for Scheduled Traffic - http://www.ieee802.org/1/pages/802.1bv.html

[2] IEEE P802.1CB – Frame Replication and Elimination for Reliability - https://1.ieee802.org/tsn/802-1cb/ 

- Very good knowledge of networking concepts and architectures

- Very good knowledge and practical experiences with Linux networking

- Good knowledge of Python, C, C++ or Rust

- Any prior experience with optimization methods and tools is a plus

- Any prior experience with TSN is a plus

- Any prior experience with SDN is a plus

- Proficient English and/or German communication skills

- Ability to work and pursue solutions under limited supervision

- Motivation to work with real hardware deployments

ermin.sakic@siemens.com

blenk@tum.de

andreas.zirkler@siemens.com

<p>The course "Data Networking" (replaces Broadband Communication Networks) of 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 will be applied and developed to present Data Networking in a blended learning format based on Podcasts (the students learn and experiment in short episodes about wireless communication and networking in day to day scenarios), online quizzes, feedback channels, recording of lecture, tutorial and discussions.</p>

<p>In order to setup and support the course Data Networking in WS 20/21, Prof. Kellerer is looking for students experienced in programming and in video tools for recording and other online tools (incl. moodle) to help him. Work can start in Sept./Oct 2020 already for preparations.</p>

Note: This work can also be be done in parallel to taking the course Data Networking in WS 20/21

<p>Programming skills; experience with online teaching tools, video tools and recording; knowledge of communication networks </p>

Arled Papa (arled.papa@tum.de)

Anna Prado (anna.prado@tum.de)

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.

Current SD-RAN platforms are only evaluated for small network dimensions due to expensive wireless hardware. However, in order to test the real scalability of the existing solutions a large number of devices needs to be deployed. To this end, an emulation mode for the control and data plane is seen as a potential solution. 

In this work, the student shall deploy a data plane user based simulator that is able to connect to an SD-RAN controller in real time through a control plane channel and report user specific statistics. The controller should be able to send singaling information back to the simulator and the effect on the user perfromance should be measured. 

The main goal of this work is to investigate the real time deployment of an SD-RAN architecture and identify potential bottlenecks regarding the control plane signaling and its effects on the users in the data plane.

• Mandatory programming skills in Python, C++ is a plus.
• Mandatory knowledge about socket programming.
• Knowledge about mobile networks 4G/5G.
• Knowlegde about event based simulators is a plus.
• Experience with google protobuffers is a plus.

Problem Statement

The human hand is one of the most versatile instruments to interact with the environment. Consequently, hand interactions play a key role for realistic interactivity in virtual environments. Most existing hand tracking systems today are vision based which require a complex instrumentalization of the environment. This constrains portability and ad-hoc usage, limiting application use cases for industry and consumers.

The goal of this thesis is to implement a glove to track a hand position in space and visualize it in real time in VR to simulate the trajectory of an object released from the hand. The overall goal of this research project is to implement a physically realistic VR simulation of the popular game beer pong to investigate whether practice in VR environments results in training effects in real life.

Tasks

- Review of related literature

yle="margin-left: 18.0pt; mso-add-space: auto; text-indent: -18.0pt; mso-list: l0 level1 lfo1;">- Prototypical development of a sensory glove

yle="margin-left: 18.0pt; mso-add-space: auto; text-indent: -18.0pt; mso-list: l0 level1 lfo1;">- Implementation of real-time hand tracking and visualization in VR (Unity)

yle="margin-left: 18.0pt; mso-add-space: auto; text-indent: -18.0pt; mso-list: l0 level1 lfo1;">- Implementation of a real-time trajectory simulation

yle="margin-left: 18.0pt; mso-add-space: auto; text-indent: -18.0pt; mso-list: l0 level1 lfo1;"> - Evaluation of the implemented trajectory simulation in VR in a user study

Preferred Qualifications

- Hardware Prototyping (IMUs, Microcontrollers, e.g. nRF52 DK)
- Experience with signal processing and filtering (i.e. Kalman filtering)
- Interest in VR (Unity, C++ or C#)
- Independent thinking and creative problem solving
Related Work

Real Time VR

https://doi.org/10.1109/38.963459

Hand Tracking and Gloves

https://ieeexplore.ieee.org/document/6223225

https://doi.org/10.1109/TIM.2008.925725

https://doi.org/10.1145/1531326.1531369

Trajectory Prediction

https://www.3delement.com/?p=610

https://web.mit.edu/16.unified/www/FALL/systems/Lab_Notes/traj.pdf

Michael Fröhlich, CDTM (TUM & LMU)

http://www.cdtm.de

Email: froehlich@cdtm.de

The advent of programmable switches and its high-level programming language such as P4 enabled many new innovative applications. Network telemetry is one of these promising applications.

In this work, the student will build a deep understanding of the state-of-the-art work related to network telemetry and data analytics using P4 programmable hardware. Mainly the focus will be on the "StarFlow" system in order to reproduce this work, analyze its strong features, and identify its limitations.

Here are more references related to StarFlow project:

1- Blog with project description: https://blog.apnic.net/2018/10/02/packet-level-network-telemetry-and-analytics-without-compromises/

2- Published Paper: https://www.usenix.org/system/files/atc18-sonchack.pdf

3-Git Repository: https://github.com/jsonch/StarFlow

-Background on P4

- C++ Programming

- Critical Thinking

Network slicing is a new emerging technique introduced in 5G to boost network performance and accomodate the new heterogeneous requirements of upcoming applications. To this end this has attracted a lot of attention in the state-of-the-art where there is vast of ongoing research in resource allocation and network efficiency in the RAN domain.

The focus of this thesis will be on development and implementation of heuristic approaches for increasing efficiency in resource allocation and network performance. 

• Strong mathematical background.
• Solid programming skills (python, matlab, c++).
• Knowledge of optimization techniques is a plus.

In this work, we plan to implement several Virtual Network Functions (VNFs) for NAT, Firewall, IDS, etc. on Virtual Machines and Contrainers. We plan to characterize the VNFs in terms of resource usage. Thereafter, we study the reconfigurability and its overhead on the performance of the VNFs. We would like to measure the metrics such as throughput, latency, etc.

programming, linux

amir.varasteh@tum.de

At the heart of many computer network planning, deployment, and
operational tasks lie hard algorithmic problems. Accordingly, over
the last decades, we have witnessed a continuous pursuit for ever
more accurate and faster algorithms. We propose an approach to
design network algorithms which is radically different from most
existing algorithms. Our approach is motivated by the observation
that most existing algorithms to solve a given hard computer net-
working problem overlook a simple yet very powerful optimization
opportunity in practice: many network algorithms are executed
repeatedly (e.g., for each virtual network request or in reaction to
user mobility), and hence with each execution, generate interesting
data: (problem,solution)-pairs. We make the case for leveraging the
potentially big data of an algorithm’s past executions to improve
and speed up future, similar solutions, by reducing the algorithm’s
search space. We study the applicability of machine learning to net-
work algorithm design, identify challenges and discuss limitations.
We empirically demonstrate the potential of machine learning net-
work algorithms in one case study, namely the embedding of
virtual networks (a packing optimization problem) using a prototype
implementation.

In future, data will be transmitted over light, especially in case of aircraft cabins. However, cabin channel models are non existent for communication based on light (LiFi). Therefore, we developed a 3D structure in blender to simulate light propagation in aircrafts. However, the model is lacking passengers. Passengers will block the light path and cause disruptions in communication.

The goal of this thesis is to extend the model by adding passengers, with the help of pre-existing models. After extending the simulator, different scenarios should be simulated and analysed. The overall goal is to show the influence of adding passengers and to derive a blockage model for LiFi.

Related Work:

Blender

https://www.blender.org/

LiFi

H. Haas, L. Yin, Y. Wang and C. Chen, "What is LiFi?," in Journal of Lightwave Technology, vol. 34, no. 6, pp. 1533-1544, 15 March15, 2016, doi: 10.1109/JLT.2015.2510021.

• Some experience in python
• Interest in 3D modeling
• Interest in new communication technologies

The goal is to investigate the impact of topology, addressing scheme and communication pattern on the learnability of a routing protocol.

In future, data will be transmitted over light, especially in case of aircraft cabins. However, cabin channel models are scarce and outdated for radio frequencies. Therefore, we developed a 3D structure in blender to simulate light propagation in aircrafts. However, the model is lacking passengers.

The goal of this thesis is to extend the model by adding passengers, with the help of pre-existing models and automated by a script. After extending the simulator, different scenarios should be simulated and analysed. The overall goal is to show the influence of adding passengers.

Tasks:

• Review of related literature
• Extend the model with people
• Compare the new channel effect
• Evaluate of the simulation results

Related Work:

Blender

https://www.blender.org/

• Some experience in python
• Interest in 3D modeling
• Interest in new communication technologies

Wireless resource allocation for networked control systems. An extension of the SotA with Gilbert-Elliot Model is the aim of this work.

The efficient embedding of virtual networks is an integral part of future communication infrastructures such as 5G/6G. In order to gain the highest revenue from their infrastructure, operators need to solve the so-called virtual network embedding problem. As this problem combines variants of well-known optimization problems, e.g., the bin-packing problem and the set covering problem, the development of fast and efficient algorithms seems to be an endless endeavor. While research has proposed a countless number of algorithms, less attention has been paid to simple problem aspects. For instance, although most work focuses on algorithms for the online embedding problem, less attention has been paid to answering questions such as how and why online and offline embedding problem solutions differ at all. Hence, this thesis targets at the question of whether time really matters. How different or how equal do existing algorithms behave when faced with online or offline variants. This study should try to reveal potential similarities or diversities between the online and offline virtual network embedding problem. To achieve this, a simple offline embedding simulator should be developed in C++ and compared to an existing online embedding simulator. The offline embedding simulator should implement at least two state of the art algorithms and compare their performance with respect to online and offline use cases.

Programming skills, interests in algorithms, strong motivation.

Goal is to implement a probabilistic graphical model to represent weighted graphs with overlapping communities.

...

...

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.

The student will develop a benchmarking tool for P4Runtime controllers. Then the performance of ONOS controller with P4Runtime southbound protocol will be evaluated and compared to that when OpenFlow is adopted as a southbound protocol.

SDN, P4, and JAVA programming skills.

More and more devices communicate wirelessly in the cabin. This includes, seat screens, passenger devices (mobile phones, laptops), sensors and actuators. As of today, the complete unlicensed spectrum is used. In future, 5G-NR-U base station can be deployed in this scenario, to offer new connectivity possiblities. This requires new coexistence mechanisms that do not put further strain on spectrum resources.

The goal of this thesis is to extend a pre-existing enhance a pre-existing simulator (based on matlab) for 5G-NR-U signal demodulation, with the help of pre-existing matlab 5G-NR-U functions. After extending the simulator, different scenarios and channel etimation techniques should be investigated, as well as several Key Performance Indicators such as Bit Error Rate, Signal-to-Interference-to-Noise-Ratio and throughput.

The overall goal is to show that interference cancellation, the underlying concept of Rate Splitting and NOMA is viable even in a challenging environment such as an aircraft cabin where the high density of radio devices is a challenge

Tasks

• Review of related literature
• Extend our simualtion from 4G to 5G
• Compare different channel estimation techniques
• Evaluate of the simulation results

Preferred Qualifications

• Experience in matlab
• Interest in signal modeling
• Interest in new communication technologies

Related Work

Interference Cancelation
https://www.researchgate.net/profile/Cheng-Xiang_Wang/publication/
224083390_Cognitive_radio_networks/links/0fcfd50b57f883862f000000/Cognitive-radio-
networks.pdf
5G-NR-U
https://de.mathworks.com/help/5g/examples/nr-pdsch-throughput.html
Channel Estimation Techniques
https://ieeexplore.ieee.org/document/1033876
http://engold.ui.ac.ir/~sabahi/Advanced%20digital%20communication/Channel%20estimation%20for
%20wireless%20OFDM%20sysytems.pdf

In this work, the student will derive analytical equations for different performance metrics related to P4 devices. The modeling will be based on queuing theory and previously conducted measurements on P4 devices. Simulations will be used to verify the derived performance model. 

Knowledge about P4, Queuing Theory, and Matlab. 

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.
Recently several new architectures have been proposed that rely on reconfigurable optics to evolve the topology over time, such as Helios[3], Projector[5], c-Through[1] or RotorNet[6].
Some of these approaches learn attributes of the traffic distribution in the network to determine topology configurations, e.g., xWeaver[4] or DeepConf[2].
A different stream of research focuses on generating adversarial input to networks or networking algorithms [7,8] to identify weak spots or improve robustness and solution quality of data-driven approaches.
The goal of this thesis is to apply the latter one to reconfigurable topology designs with the main focus on the data-driven variants (xWeaver) to benchmark their performance and to improve their solution quality.
The thesis builds upon an existing flow-level simulator in Python and initial algorithms that generate adversarial inputs for networking problems.

[1] G. Wang et al., “c-Through: Part-time Optics in Data Centers,” presented at the ACM SIGCOMM, 2010, p. 12.
[2] S. Salman, C. Streiffer, H. Chen, T. Benson, and A. Kadav, “DeepConf: Automating Data Center Network Topologies Management with Machine Learning,” in Proceedings of the 2018 Workshop on Network Meets AI & ML, New York, NY, USA, 2018, pp. 8–14, doi: 10.1145/3229543.3229554.
[3] N. Farrington et al., “Helios: A Hybrid Electrical/Optical Switch Architecture for Modular Data Centers,” pp. 1–12, 2010.
[4] M. Wang et al., “Neural Network Meets DCN: Traffic-driven Topology Adaptation with Deep Learning,” Proceedings of the ACM on Measurement and Analysis of Computing Systems, vol. 2, no. 2, pp. 1–25, Jun. 2018, doi: 10.1145/3224421.
[5] M. Ghobadi et al., “ProjecToR: Agile Reconfigurable Data Center Interconnect,” 2016, pp. 216–229, doi: 10.1145/2934872.2934911.
[6] W. M. Mellette et al., “RotorNet: A Scalable, Low-complexity, Optical Datacenter Network,” 2017, pp. 267–280, doi: 10.1145/3098822.3098838.
[7] 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.
[8] 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.

Decentralized channel access strategies with Reinforcement Learning when the agents are networked control systems. We assume slot based centralized scheduling is not feasible.

Sensors need to observe the channel outcome and also plant state to decide on action.

Python programming, Basics of Communication Networks, Reinforcement Learning Understanding

The Boarder Gateway Protocol (BGP) lies at the heart of the Internet. Internet routers use BGP to exchange routing information with neighbors. Based on their routing policy, current updates, and internal network routes, routers populate their local routing information base. The final paths are pushed to the forwarding information base (FIB). For fast matching, FIBs are implemented with expensive TCAM. In order to use TCAM space efficiently, aggregation algorithms compress the RIB before pushing the final paths to the FIB. However, each BGP update may demand a change of the FIB, leading to expensive compression and decompression tasks. Hence, this work focusses on the possibility of machine learning to enhance the FIB compression routine.

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).

Network slicing is a new emerging technique introduced in 5G to boost network performance and accomodate the new heterogeneous requirements of upcoming applications. To this end this has attracted a lot of attention in the state-of-the-art where there is vast of ongoing research in resource allocation and network efficiency in the RAN domain.

The focus of this thesis will be on implementation of an event based slicing simulator and built-up of a deep reinforcement learning framework that interacts with the schedulers of the event based simulator. The final goal is the ability to predict the scheduler type and adjust the scheduling decisions accordingly to boost the network performace.

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.

Introduction to Software Defined Networking (SDN) and Network Virtualization paradigms has brought new challenges to tackle. In this environment, network switches communicate to SDN controllers through a hypervisor. Therefore, considering a network graph, three questions may come up: 1) Where to deploy SDN controllers? 2) Where to deploy SDN hypervirors? 3) How to connect network switches to the respective SDN controllers to meet the traffic dynamicity and QoS requirements?

In this work, we plan to design and evaluate an optimization framework to optimally answer the above questions.

Mathematical Optimization, Algorithms, Java / Python

amir.varasteh@tum.de

Optical core networks deploy fixed grid in order to accommodate their demands. The trend is however, migrate towards flexgrid networks able to use as much spectrum as required by the demand allowing a better use of the spectrum. However, the Routing and Wavelength Assignment (RWA) paradigm becomes more complex as it has to find the suitable spectrum of the demand.

This thesis will consider different multiplexing alternatives: space division, band division multiplexing, etc.

The objective is to compare the performance, throughput, BoM, etc. of the different alternatives for different networks and demand evolution matrices.

Java programming skills

Basic knowledge of flexible optical networks

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.

yle="background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-origin: initial; background-clip: initial;"> 

yle="background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-origin: initial; background-clip: initial;">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. 

yle="background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-origin: initial; background-clip: initial;"> 

yle="background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-origin: initial; background-clip: initial;">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.

yle="margin-bottom: 8.0pt; line-height: 105%;"> 

yle="background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-origin: initial; background-clip: initial;">The tasks in detail are as follows.

yle="background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-origin: initial; background-clip: initial;">Setup a waveform simulation environment and extend to elaborate test cases

yle="background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-origin: initial; background-clip: initial;">Run the generated waveforms through the algorithms

yle="background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-origin: initial; background-clip: initial;">Analyze and compare the results:

yle="background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-origin: initial; background-clip: initial;">Frequency domain

yle="background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-origin: initial; background-clip: initial;">Statistically (histogram, etc)

yle="background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-origin: initial; background-clip: initial;">Time domain

yle="background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-origin: initial; background-clip: initial;">Consistency of the results

yle="background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-origin: initial; background-clip: initial;">Evaluate the estimation of the BER (bit error rate)

yle="background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-origin: initial; background-clip: initial;">Identify the limitations of the dual-dirac model

yle="background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-origin: initial; background-clip: initial;">Compare dual-dirac model results with a calculation based on the step response estimation

yle="background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-origin: initial; background-clip: initial;">Generate new waveforms based on the analysis

yle="background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-origin: initial; background-clip: initial;">Summarize findings

The task is to predict the trajectory (or path) of a mobile user using some machine learning algorithms (e.g. reinforcement learning or recurrent neural networks). Using this information one can then use this for eg. In LiFi to predict when the moving user will block the light for another user. Or eg. In 5G to predict when to perform handovers.

The tasks are:

1. Create the training dataset by implementing an existing mathematical mobility model using Python (SLAW model).
2. Use multiple learning techniques to predict the trajectory of a mobile user
3. The goal is to compare the learning techniques to see how good the prediction can be and in which applications they can be used and how this would impact handovers in LiFi and 5G.

Related work:

Lee, K., Hong, S., Kim, S.J., Rhee, I. and Chong, S., 2009, April. Slaw: A new mobility model for human walks. In IEEE INFOCOM 2009 (pp. 855-863). IEEE.

Gebrie, H., Farooq, H. and Imran, A., 2019, May. What machine learning predictor performs best for mobility prediction in cellular networks?. In 2019 IEEE International Conference on Communications Workshops (ICC Workshops) (pp. 1-6). IEEE.

• Python knowledge

• Knowledge in machine learning algorithms (especially RNNs)

• Interest in learning about mobility models

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, Go.
• Knowledge about mobile networking is necessary.
• Knowlegde about 4G/5G architecture is a plus.

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 this internship, this idea will be put to the test in a physical testbed, which already implements a flexible functional split. The testbed will be extended to support multiple DUs and UEs. Furthermore, a coordinated scheduling scheme will be implemented to that centralized functions are able to mitigate their mutual interference. Finally, experiments to measure the effectiveness of this approach will be conducted.

LiFi (Light-fidelity) is becoming an increasingly important technology as the unlicensed ISM band gets overcrowded. LiFi, with its high data rates, emerges as a promising solution to provide connectivity using LED lights thereby reducing the interference with other wireless technologies. Due to the unique properties of a visible light communciation system, the position of theaccess points/LEDs has a significant effect on the network coverage and illumination level in an indoor area.

This work involves modelling an indoor office using Blender (a 3D modelling software) and analyzing the effects of different LED placement strategies.The goal is to answer the question: Which design is preferred for LiFi access point placement in an office environment?

Related Reading:

Blender

https://www.blender.org/

LiFi

H. Haas, L. Yin, Y. Wang and C. Chen, "What is LiFi?," in Journal of Lightwave Technology, vol. 34, no. 6, pp. 1533-1544, 15 March15, 2016, doi: 10.1109/JLT.2015.2510021.

• Python
• Interest in new communication technologies
• Interest in 3D modelling

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.

This research internship looks at Quanktum Key Distribution exchange in a wide area pan-European network. Different architectures, routing and spectrum allocation methods need to be considered, keeping in mind both technological and economic constraints of such a combined deployment.

This work is helpful for Optical Networking as well as satellite network providers in order to provide secure data transmissions over large region multi-national governmental organizations, banks and security agencies.

In this internship/bachelor thesis, a student will research different lightpath reconfiguration algorithms models and their applications to flexible optical networking. Then, they will create an evaluation framework and finally simulate a network planning study.  The results will be investigated to draw useful conclusions.

Must Have

• Basic knowledge of graph theory, communication networks and optical networks
• Independent worker
• Working knowledge of Python
• Willingness to learn

Good to Have

• Knowledge of Java
• Familiarity with NetConf/YANG

To apply, please send your CV and grade transcripts to kireet.patri@tum.de

...

Flexibility is an indispensable feature of future communication networks and technologies. Only flexible networks allow coping with the fast-changing demands of future network applications and use cases like 5G/6G. In order to quantify the flexibility of networks, we have recently presented a new flexibility measurement framework. As many problems in network communication are studied on graphs, this thesis should investigate the impact of topologies on network flexibility by applying the concepts proposed in our framework. In detail, different networking use cases (like network programmability, network failures, and network verification) should be analyzed for a variety of network topologies. As graph measures are one way to characterize network graphs, the thesis should analyze the relation between graph features and network flexibility. It will be expected that the studies will be data-intensive; hence, the student should bring preliminaries in carrying out automated and data-intensive analysis. Finally, this thesis should study the predictability of flexibility given topologies. For this, it should apply supervised machine learning models to answer the question of whether we can predict the flexibility of given problems for specific networking problems.

Flexibility is an indispensable feature of future communication networks and technologies. Only flexible networks allow to cope with the fast-changing demands of future network applications and use cases like 5G/6G. In order to quantify the flexibility of networks, we have recently presented a new flexibility measurement framework. Many problems in network communication are studied on graphs. This internship centers around a particular use case of Controller Placement Problem and investigate the impact of topologies on flexibility of dynamic control plane by applying the concepts as proposed in our framework. As graph measures are one way to characterize network graphs, the thesis should analyze the relation between graph features and flexibility. It will be expected that the studies will be data intensive; hence, the student should bring preliminaries in carrying out automated and data intensive analysis. Finally, this internship should study the predictability of flexibility given topologies. For this, it should apply supervised machine learning models to answer the question whether we can predict the flexibility of dynamic control plane.

P4 is a novel language designed for programming packet processors in a high-level language. Currently, it can be used to program different types of networking devices. https://p4.org/

Our goal in this thesis is to investigate the optimal placement of different network functions (or P4 programs) into different P4 devices based on the characteristics of the P4 programs and the P4 devices. 

In this work, the student will formulate the placement problem as an optimization problem and solve it for different objectives and under the relevant constraints.

- Good theoretical background on optimization.

- Background on P4 language is a plus.

The Wireless Sensor Networks lab offers the opportunity to develop software solutions for the wireless sensor networking system, targeting innovative applications. For the next semester, a position is available to assist the participants in learning the programming environment and during the project development phase. The lab is planned to be held every Tuesday from 15:00 to 18:00.

Solid knowledge in Wireless Communication: PHY, MAC, and network layers. Solid programming skills: C/C++. Linux knowledge. Experience with embedded systems and microcontroller programming knowledge is preferable.

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

Networked Control Systems, i. e. the interconnection of a control system over a communication network, are a fundamental building block of future industrial automation systems. To study NCS, the NCSbench platform [1,2] was developed allowing performance measurements of a real NCS. The platform is built using Lego, is developed in python, communicates over standard IP network interfaces and is fully open-source.

In order to further develop the platform, a working position is available.

[1] https://github.com/tum-lkn/NCSbench/wiki

[2] S. Zoppi, O. Ayan, F. Molinari; Z. Music, S. Gallenmüller, G. Carle, W. Kellerer, NCSbench: Reproducible Benchmarking Platform for Networked Control Systems. IEEE Consumer Communications & Networking Conference, 2020

This work will consist of a significant amount of programming and also of processing the measurement data.

Requirements:

• Good knowledge of wireless systems and protocols (WLAN, WSN).
• Basic knowledge of LTI control systems.
• Strong C and Python programming skills.
• Experience with Linux systems is recommended.
• Experience with python data processing tools is beneficial.

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

yle="text-align: justify;">Flexible optical networks achieve a significant increase of network capacity by assigning as much spectrum to the demands as needed. With advances in transponders supporting software tunable channel configurations, network planners are able to select the best combination of data rate, modulation format and forward error correction (FEC) for each light-path. However, the impact of failures in these networks is higher due to the closeness between channels as well as the low OSNR margins.

yle="text-align: justify;">Hence, in this thesis we will look into the monitoring capabilities of flexible optical networks provided by existing components as well as extra monitoring equipment. All the gathered monitoring information should be processed by machine learning based classification tools such that fast and efficient failure diagnosis can be achieved.  

Programming skills (Python)

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

amir.varasteh@tum.de

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)

The yle="color: #333333; font-family: Arial, sans-serif; font-size: 14px;">Software-Defined Networking (SDN) lab offers the opportunity to work with real hardware on interesting and entertaining projects related to the SDN network paradigm. For the next semester, a position is available to assist the teaching assistants for the lab (definition and preparation of the assignments, preparation of the hardware, etc.).

yle="list-style-type: square; margin-left: 0px; padding-left: 20px; color: #333333; font-family: Arial, sans-serif; font-size: 14px;">
• yle="padding-left: 0.3em;">Solid knowledge in computer networking (TCP/IP, SDN)
• yle="padding-left: 0.3em;">Solid knowledge of networking tools and Linux (iperf, ssh, etc)
• yle="padding-left: 0.3em;">Good programming skills: C/C++, Python