direkt zum Inhalt springen

direkt zum Hauptnavigationsmenü

Sie sind hier

TU Berlin

Page Content

There is no English translation for this web page.

Work Package I


Massive MIMO Channel Covariance and the Common Eigendecomposition Property: In Phase I CoSIP project we developed efficient channel estimation methods for massive uniform linear arrays (ULA) by exploiting the Toeplitz structure of the covariance matrix resulting from this type of arrays. In particular, such covariances always admit an eigendecomposition over FFT matrices, regardless of the user location and its environment. In Work Package I, we ask if a similar behaviour holds for generic massive arrays or not. The existence of a common eigendecomposition among the covariances generated with an array highly simplifies the necessary signal processing by reducing instantaneous channel and covariance estimation overload and allows efficient user scheduling in wireless networks with massive antenna arrays.

Work Package II


Learning the “Soft-Topology” of a Wireless Network: In several wireless networks problems, learning the network topology plays a fundamental role in order to enable power and rate allocation, multi-hop relaying, interference management, and distributed massive MIMO beamforming. Furthermore, the concept of “neighbor” in wireless networks is not directly related to Euclidean distance. Two nodes at locations x and y are neighbors if their channel strength, describing the mean power attenuation of a signal transmitted at point x and received at point y (assuming ideal isotropic antennas), is large. In contrast, the two nodes x, y are not neighbors if their channel strength) is small. While the concept of “large” and “small” depends on the application and on the context, the central idea to point out here is that the channel strength, sampled at the network nodes location, yields a “soft-topology” that must be learned by the network controller. In Work Package II, we aim to establish a distributed efficient spatial sampling scheme and to interpolate the samples in order to learn the channel strengths of all the node pairs in the network coverage region.




Work Package III


Enhanced Localization via Multi-Band Splicing: Localization in wireless networks has become a more and more important issue and has found applications in WiFi-based indoor localization, smart home occupancy, device-to-device link scheduling and gesture recognition to name a few. A standard way to achieve radio localization consists of estimating the angle of arrival (AoA) and time-of-flight (ToF) with respect to a beacon source and combining this information over multiple sources. In a wide-band transciever such as a WiFi device with a bandwidth of W, the ToF can be estimated with a resolution not smaller than 1/W. When W is small, this results in coarse estimation of the ToF and hence poor localization, while increasing W potentially enhances localization. The goal of this project is to use WiFi pilot signals over multiple frequency bands to increase the bandwidth and achieve finer localization. A major challenge here is that usually the raw WiFi data is prone to several phase and amplitude distortions, which makes CIR estimation a difficult task. We leverage channel sparsity as well as various signal processing tools to overcome such distortions and obtain a good estimate of the channel delay profile.

Zusatzinformationen / Extras

Quick Access:

Schnellnavigation zur Seite über Nummerneingabe