Sparsity is an attribute present in a myriad of natural signals and systems, occurring either
inherently or after a suitable projection. Such signals with lots of zeros possess minimal …

Data originating from devices and sensors in Internet of Things scenarios can often be
modeled as sparse signals. In this paper, we provide new source compression schemes for …

Subtractive dither is a powerful method for removing the signal dependence of quantization
noise for coarsely quantized signals. However, estimation from dithered measurements …

The received signal strength (RSS)-based target localization is an important field of research
with numerous applications in wireless sensor networks. By exploiting the sparsity of …

Line spectral estimation (LSE) is a fundamental problem in signal processing fields, as it
arises in various fields such as radar signal processing and communication fields. This …

Massive multi-user (MU) multiple-input multiple-output (MIMO) will be a core technology in
next-generation wireless systems. By equipping the infrastructure base-stations (BSs) with …

Recently approximate message passing algorithms have gained a lot of attention for solving
problems with a known prior distribution and an observation that is described by a noisy …

Limitations or constraints in signal acquisition systems often lead to signals that are
measured in a compressive manner, ie involving dimensionality reduction, or information …

This thesis studies digital compression of sparse signals via information-theoretic limits and
compressed sensing algorithms. A rate-distortion function with multiple constraints is …