If you are interested in a thesis in statistical signal processing/communications/biomedicine, but can't find a
suitable topic below, please feel free to contact us.
Übrigens: Auch wenn ein Thema auf Englisch ausgeschrieben ist, können Sie Ihre Arbeit auf Deutsch schreiben.
Bachelor/Master's level
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Description
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Credit: Wang et al., IEEE Journal of Selected Topics in
Signal Processing (2011)
Since the usable radio spectrum is of limited physical extent it is a resource of 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.
Credit: Wang et al., IEEE Journal of Selected Topics in
Signal Processing (2011)
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.
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Prerequisites
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Knowledge of statistical signal processing is essential and a background in digital communications is desirable.
Prior experience with MATLAB or Python is helpful.
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Contact
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Stefanie Horstmann, [javascript protected email address]
Master's level
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Description
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Credit: Zahir, Talha, et al.,
IEEE communications surveys & tutorials (2013)
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.
Credit: Yucek, Tevfik, and Huseyin, Arslan,
IEEE communications surveys & tutorials (2009)
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.
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Prerequisites
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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.
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Contact
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Mohammad Soleymani, [javascript protected email address]
Master's level
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Description
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Credit: blogs.scientificamerican.com
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.
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Prerequisites
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Knowledge of statistical signal processing and linear algebra. Experience with MATLAB is helpful.
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Contact
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Tanuj Hasija, [javascript protected email address]
Master's level
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Description
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Credit: Liu Chunshan, et al. IEEE Transactions on Wireless
Communications (2017).
Beamforming is a spatial filtering technique commonly used in wireless communications
and radar systems, which allows signal transmission and reception to be
targeted to particular angles. It may also be applied to detection, such as a radar
scanning certain airspace to detect targets, or a base station broadcasting pilot signals
for itself to be detected. As illustrated in the figures, the transmitter can either
use a single wide beam or a sequence of sharper beams to cover the intended angle
interval. The question is, which strategy is better in the sense of detection power?
Credit: Liu Chunshan, et al. IEEE Transactions on Wireless
Communications (2017).
Under a certain power constraint, the sharper the beam is, the higher an SNR can be
achieved. However, as the airspace is divided into more cells, the observation time
allocated to each cell is averaged down, suggesting fewer samples will be available
for each scan. The goal of the project is to figure out the best beamforming strategy
for detection, namely: How many beamformers should be used to provide the optimal
detection performance? To solve this, the distribution of the detector must be
analyzed to investigate how its detection power is impacted by SNR and number of
samples. Subsequently, optimization of its performance will be carried out to solve
this problem. The work may be started with certain simpler detectors (e.g., John's
test). Relevant mathematical tools will be provided to you to enable such statistical
analysis.
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Prerequisites
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Some background in statistics and linear algebra is required. Prior experience with
Matlab will also be very helpful.
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Contact
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Yu-Hang Xiao, [javascript protected email address]
