Open Topics in the Area of Artificial Intelligence/Machine Learning (AI/ML)

This page lists all open topics in the area of AI/ML for communication networks. Interested students can contact the supervisor(s) to ask questions or discuss the details of a topic. It is also possible to propose your own topic in the area. An overview of AI/ML applications in networking can be found in [1].

Supervisors of our AI/ML work group: Christian Vielhaus, Johannes Busch, Vincent Latzko

Traffic Prediction with Machine Learning

Supervisor: Christian Vielhaus

Predicting network traffic facilitates various management and orchestration problems in communication networks. Traffic volume prediction can be formulated as a pure Time Series Forecasting (TSF) problem, in which the temporal evolution of a data rate in the near future is forecast. In contrast to that, regression algorithms use correlated statistical traffic features to approximate a mapping between the features and the expected traffic volume. There are other, related problems such as the prediction or classification of the lifetime of a flow or its size. Please refer to [1] for an overview of existing literature on the topic.

The task for the student is to design and implement a traffic prediction algorithm based on Machine Learning (ML), which includes

  • creating an overview of publicly available datasets,
  • analyzing, comparing and preprocessing available datasets,
  • implementing a TSF and/or regression ML algorithm with known frameworks and
  • evaluating the results.

It is possible to focus on specific aspects of the task.

  • Starting time: Immediately
  • Student/Bachelor or Diploma/Master thesis
  • Required skills: Python, basic knowledge about ML

Joined Optimization of Traffic Signaling and Vehicle Speed Advisory in V2I-Enabled Traffic

Supervisor: Johannes Busch

With emerging Vehicle-to-Infrastructure (V2I) communication technologies, such as IEEE 802.11p or 5G, arises the opportunity for an intelligent traffic infrastructure that is able to quickly adapt to the current state of the traffic system.Through the exchange of information between individual vehicles and the traffic infrastructure, traffic signaling could be tailored to individual vehicles. Furthermore the traffic infrastructure could advise drivers on appropriate driving velocities. These measures could improve traffic flow and therefore mitigate environmental and economic repercussions of traffic systems. However, the optimization of large distributed systems under the availability of intricate knowledge about the system state is not trivial and novel control paradigms need to be explored. A promising candidate could be Deep Reinforcement Learning (DRL), which has shown to be able to solve many intricate control problems by learning from interaction and has also successfully been applied to traffic control systems.

In this Diploma/Master thesis, a joint optimization of traffic signaling and speed advisory with DRL should be explored. Subtasks include:

  • A literature review of the current state of the art of intelligent traffic control, V2I, and DRL.
  • An extension of an existing traffic simulator to enable the joint control of traffic signaling and speed advisory, under the knowledge of the current state of the traffic system.
  • The optimization of the developed simulation with an appropriate DRL algorithm. Preferably, the proposed solution should be scalable to large infrastructures.
  • An experimental review of the benefits of the developed intelligent V2I-enabled traffic control system. Various experimental setups could be explored here. For instance, the scalability to large traffic systems or the influence of different penetration rates of V2I-enabled vehicles could be two particularly interesting paths to explore.

Prospective students should have previous knowledge in the Python programming language and a keen interest and previous experience in Machine Learning (preferably DRL).

  • Starting time: Immediately
  • Diploma/Master thesis
  • Required skills: Python, experience with ML

Congestion Control with Machine Learning

Supervisor: Christian Vielhaus

Network congestion [2] occurs when a node or link has to process more traffic than it can handle, causing undesired delays and congestion losses in queues. Congestion Control (CC) algorithms, either running in endpoints or within the network, mitigate congestion and are essential for sustaining the quality of service of networks today.

In recent years, CC algorithms made use of Machine Learning (ML) to improve their control decisions. Interested students can work on an ML-aided (supervised learning) sender-side congestion control algorithm.

  • Starting time: Immediately
  • Student/Bachelor or Diploma/Master thesis
  • Required skills: Python, experience with ML

[1] A Comprehensive Survey on Machine Learning for Networking: Evolution, Applications and Research Opportunities
[2] Network congestion