Theses

The following are some examples of topics we offer for Bachelor's and Master's theses. If you cannot find a suitable topic but would like to work on something related to data science, machine learning, or statistical signal processing, with applications in mobile communications, neuroscience, or medicine, please talk to us.

Übrigens: Wir schreiben unsere Themen generell auf Englisch aus. Sie dürfen Ihre Abschlussarbeit bei uns aber auch gerne auf Deutsch schreiben.

Signal detection with application to Cognitive Radio

suitable for: Bachelor's and Master's students

Prerequisites: Knowledge of statistical signal processing is essential and a background in digital communications is desirable. Prior experience with MATLAB or Python is helpful.
Image Credit: Wang et al., IEEE Journal of Selected Topics in
Signal Processing (2011)
Contact: Stefanie Horstmann

Since the usable radio spectrum is of limited physical extent it is a resource in high demand. To manage its access it is divided into sub-bands and has licenses assigned to them. However, the licensed frequency bands are typically underutilized. In order to improve the wireless spectrum utilization, cognitive radio (CR) is a new communications paradigm that manages the spectrum access dynamically. Hence, unused license bands are accessed by “cognitive” users opportunistically. To provide interference-free communication for all users vacant frequency sub-bands have to be detected reliably.

A robust spectrum sensing technique is based on cyclostationary (CS) feature detection. Since digital communication schemes produce CS signals, this property can be exploited when deciding whether a given frequency sub-band is occupied. In practice, however, we have to deal with almost-CS signals, which are much trickier to detect. In this thesis, you will look at detectors of (almost-) cyclostationarity. Theoretical questions can include the implementation of multiple hypothesis tests to detect the presence of an (almost-) CS signal or the estimation of the signal's cycle period. Alternatively, practical aspects can be explored by implementing different detectors on a hardware testbed and evaluating their performance under realistic conditions.

Fusion of brain imaging data from different modalities

suitable for: Master's students

Prerequisites: Knowledge of statistical signal processing and linear algebra. Experience with MATLAB or Python is helpful.
Image Credit: shutterstock.com
Contact: Tanuj Hasija

In biomedical imaging for the study of brain function, an increasing number of studies are collecting multiple measurements from different modalities, in particular, functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and structural MRI (sMRI). All of these are noninvasive brain imaging techniques: fMRI measures the changes in blood-oxygenation in the brain, EEG measures the brain electrical field through the scalp, and sMRI provides information about the type of brain tissue. These modalities provide complementary information. For instance, fMRI has very good spatial resolution, but bad temporal resolution, whereas EEG has high temporal resolution but poor spatial localization. It is thus of interest to fuse the measurements obtained from these different techniques to combine their respective advantages.

Approaches for data fusion can be classified as either model based or data driven. Model-based approaches require detailed a priori knowledge about the experiment to be performed and the properties of the data. When performing complex experiments, the underlying dynamics become very difficult to model, in which case data-driven analysis methods (e.g., correlation analysis techniques such as CCA) are to be preferred. In this thesis, you will investigate different data-driven techniques for making group inferences. For instance, we might be interested in analyzing fMRI data from the same subjects scanned at different alcohol levels while performing a given task.

Interference management in wireless communication

suitable for: Master's students

Prerequisites: Knowledge of wireless communications and mathematical tools, i.e., linear algebra and/or optimization methods is required. Depending on your background, scope and focus of the task can be adjusted. Some prior experience with MATLAB is also expected.
Image Credit: Zahir, Talha, et al., IEEE Communications Surveys & Tutorials (2013) and  Yucek, Tevfik, and Huseyin, Arslan, IEEE Communications Surveys & Tutorials (2009)
Contact: Mohammad Soleymani

Interference from other transmitters is the main bottleneck for most wireless communication systems, which are called interference-limited systems. Due to the bandwidth shortage, these systems must tolerate some interference in order to enhance the spectrum usage. There are different approaches to handle interference in wireless systems. Among them is employing improper signaling. In improper signals, the powers of the real and imaginary parts are not equal, and/or there is correlation between the real and imaginary parts of the signal. The capacity-achieving signal in traditional systems is a proper signal; however, it has been shown that improper signaling can increase the rate in interference-limited systems.

In this thesis, you will study the performance of an interference management technique in a wireless communication system. To this end, we first define a scenario in which the system is interference-limited. For instance, we can consider an underlay/overlay cognitive radio (CR) system. In CR systems, licensed or primary users (PU) share the spectrum with unlicensed or secondary users (SU) under the constraint that the PU's communications are not affected by the SU's transmission. In such system, we should design the transmission strategy of SU to get the desired system performance. Moreover, different transmission techniques (e.g., OFDM and single carrier) can be considered in the thesis. You will consider different interference management techniques including improper signaling and interference alignment. Then, you will optimize the performance of the defined scenario by analytical tools. Finally, you will evaluate your results by simulations using MATLAB.