direkt zum Inhalt springen

direkt zum Hauptnavigationsmenü

Sie sind hier

TU Berlin

Inhalt des Dokuments

Aktuelle Forschungsprojekte

A.v.H. - Prof. Caire - Alexander von Humboldt Professorship

A.v.H. - Prof. Caire - Alexander von Humboldt Professorship

  • PI: G. Caire
  • Total budget: EUR 3,500,000
  • Activity: 2014 -- 2019
  • Title: "Foundations and Architectures for the Next Generation of Wireless Networks''
  • Progress reports201420152016

CARENET - Content-Aware Wireless Networks: Fundamental Limits, Algorithms, and Architectures

CARENET – Content-Aware Wireless Networks
The CARENET project has received funding from European Research Council (ERC) under the European Union with call details Advanced Grants (AdG), PE7, ERC-2017-ADG.

  • Project Title: CARENET :Content-Aware Wireless Networks: Fundamental Limits, Algorithms, and Architectures
  • Project Acronym: CARENET
  • ID Grant Number: 789190
  • Principal Investigator: Giuseppe Caire
  • Host Institution: TU Berlin
  • Starting date: Oct 2018
  • Project Duration: 5 years
  • Total Budget: 2,497,500 EUR
  • Additional Information: CARENET page

CoSIP - Compressed Sensing and Information Processing

CoSIP - DFG Special Focus Program: Compressed Sensing and Information Processing

  • co-PI: G. Caire, G. Kutyniok (TUB), and G. Wunder (Freie Universität Berlin)
  • Total budget: EUR 420,000
  • Activity: 01/07/2015 -- 30/06/2018
  • Title: "Compressed Sensing for massive MIMO with structured channels”
  • Project Summary: In this proposal we address the key problems which prevent the efficient and economically viable implementation of Massive MIMO, including the transmitter/receiver sampling complexity, the problem of pilot contamination, and the problem of channel estimation both in TDD and in FDD systems. The key idea of this proposal is that a signi cant dimensionality reduction (and consequently, complexity reduction) in the Massive MIMO frontend processing can be achieved by leveraging the structure of the propagation channels between the base station antenna array and the users. These channels are argued to exhibit sparsity in the angular and delay domain. In short, especially when communication takes place in the mm-waves range, the propagation occurs along discrete multipath components, each of which is characterized by an angle of departure (AoD) and a delay. This inherent sparsity can be leveraged by modern CS algorithms, operating at much better complexity/performance trade off than conventional front-end schemes. Here, sparsity typically means that only a few samples of the signal are actually non-zero, when the signal is represented in a suitable "sparsifying basis". In general, the location of the non-zero components (relative to the signal basis elements) is not known a priori. This new paradigm has been an intriguing topic in mathematics and signal processing in recent years. Note that sparsity-based concepts have been successfully applied in specific communication problems, e.g., the "peak power control problem", the "channel impulse response estimation problem", the "neighbor discovery problem in ad-hoc networks", the "detection of spectral holes in cognitive radio", the "MIMO Radar direction of arrival problem", and several other applications. We will show that leveraging sparsity in communication signals is a viable approach to Massive MIMO implementation with affordable complexity.  The theory and the algorithms developed in this project will therefore lay the foundations for a new generation of air interfaces able to handle a very large number of Tx antennas (in the DL) and Rx antennas (in the UL, or in the channel estimation phase), thus addressing the challenges and the spectral efficiency target performance of 5G networks. As anticipated before in this research proposal we focus on the Massive MIMO scenario, while it is envisioned that in later follow-up phases the C-RAN and DAS architectures (many jointly processed antennas, but physically distributed over the network coverage region) will be also investigated.
CoSIP - DFG Special Focus Program: Compressed Sensing and Information Processing
  • co-PI: P. Jung (TUB), D. Gross (University of Cologne) and F. Krahmer (Technical University of Munich)
  • Total budget: 
  • Activity: 01/07/2015 -- 30/06/2018
  • Title: "Bilinear Compressed Sensing”
  • Project Summary: The theory of compressed sensing (CS) has shown that a substantial reduction in sampling and storage complexity can be achieved in many relevant linear and non–adaptive estimation problems. Recent theoretical developments have also put the analysis of a whole range of practical non-linear problems within reach. Examples include blind decoding of wireless signals under channel uncertainties, recovery of images from fuzzy snapshots without precise knowledge of the blurring kernel, or more general model uncertainties in conventional CS. The unifying feature of these tasks is that the signal is accessible only through an uncalibrated system, whose description is partially unknown at the time of measurement. Mathematically, one set of parameters (the channel, the kernel, the sensing matrix) is coupled in a multiplicative way to the signal – giving rise to an inherent emph{bilinear structure}. While in conventional CS such model uncertainties inevitably degrade the quality of the recovery, the novel approach is to combine bilinearity and compressibility in order to simultaneously estimate both the signal and the model parameters.The theory of bilinear CS is only at its beginnings, but has recently garnered a significant amount of attention and is developing rapidly. (Because it unites and extends the first two structural assumptions considered in the CS community – sparsity and low-rank – it is sometimes refered to as emph{compressed sensing 3.0}). While we believe that engineering applications are within reach over the duration of the Priority Program, considerable mathematical problems remain to be addressed. Our team of three principal investigators will work towards a comprehensive theory for bilinear CS. On the one hand, we will study, in an abstract context, recovery properties for random subsampling of bilinear maps, as well as bilinear maps of vectors under random subspace conditions. On the other hand, we will work to develop adapted techniques for specific applications, focusing on wireless communication, but also touching on imaging and spectroscopy.

CoSIP - Compressed Sensing and Information Processing - Phase II

CoSIP - DFG Special Focus Program: Compressed Sensing and Information Processing - Phase II

  • co-PI: G. Kutyniok (TUB)
  • Total budget: 
  • Activity
  • Title "Compressed Sensing Algorithms for Structured Massive MIMO - Phase II"
  • Project Summary:
Phase I of this project focused on exploiting the structure of multipath propagation to solve the dimensionality bottleneck problem of massive MIMO. Our results in Phase I clearly indicate that the structure to be exploited resides in the "invariants" of the channel, i.e., in those quantities that remains constant over a large time interval and a large frequency bandwidth. In particular, these invariants are contained, implicitly or explicitly, in the channel second-order statistics. Remarkably, our intuition and findings during the first 3 years of the project have become "instant classics" and literally thousands of papers have followed in our footprints, such that today the approaches that we have advocated at the beginning of the first funding phase have become mainstream.
In Phase II, we build on the experience and on the successes of Phase I and we broaden our horizon from the single massive MIMO system to a whole wireless network, where the large dimensionality arising from large number of users and base station antennas is the salient feature. We identify three new overarching objectives and lay out our workplan organized in three corresponding work packages. The first focuses on the efficient representation of large dimensional channel vectors for general array geometries, where the aim is to generalize Szego’s theorem on large Toeplitz matrices to families of non-Toeplitz Covariance matrices generated by given array manifolds. The second consider the distributed sampling and learning of the path gain function between any two points of a given coverage area, referred to as network "soft" topology. Finally, the third consider a bilinear compressed sensing problem arising from multichannel splicing, that is, combining multiple narrowband observations in order to obtain a wideband measurement of the channel impulse response and achieve a sufficiently high timing resolution such that precise ranging for indoor position using conventional RF signals is possible. We outline mathematically precise problem definitions and concrete methodologies to address the problems, corroborated by preliminary results and previous background results obtained by the PI in their previous work. As such, although the objective of this proposal are challenging, we are confident that significant progress can be made in time span of the project.



SERENA - gan-on-Silicon Efficient mm-wave euRopean systEm iNtegration plAtform

SERENA - European Union Horizon 2020 project:
gan-on-silicon efficient mm-wave european system integration platform
  • 9 partners from 5 European countries
  • TUB PI: G. Caire
  • Total budget: EUR 3,910,000 (TUB budget EUR 199,000)
  • Activity: 01/01/2018 -- 31/12/2020
  • Title: "gan-on-Silicon Efficient mm-wave euRopean systEm iNtegration plAtform”
  • Grant agreement Number: 779305
  • Project Summary: The project will develop a system architecture and technology platform by using an integrated approach. Further, SERENA will combine advancements in hybrid analogue/digital mm-wave beam-steering system architectures with a completely European based semiconductor supply chain. Finally, the project team will foster an inter-disciplinary design approach with a strong emphasis on multi-physics simulations and predictive co-design to show the unique capabilities of the SERENA technology.
  • Additional Information: SERENA presentation and www.serena-h2020.eu

Non-Negative Structured Regression (Non-Negative Structured Regression in Communication and Data Science)

Non-Negative Structured Regression - DAAD Programm: Fachbezogene Partnerschaften mit Hochschulen in Entwicklungsländern

Eine Hochschulkooperationen mit dem African Institute for Mathematical Sciences (AIMS) Südafrika, Kamerun, Ghana

  • co-PI: G. Caire, P. Jung
  • Gesamtbudget: EUR 200,000
  • Laufzeit: 01/02/2018 - 31/01/2022
  • Titel: Nicht-Negative, strukturierte Regressionmethoden für Kommunikation and Data Science Anwendungen
  • Projetktbeschreibung: In diesem Projekt sollen effiziente Algorithmen zur Rekonstruktion spezieller kodierter Daten aus dem Bereich der drahtlosen Übertragung und Netzwerkdaten-muster untersucht und entwickelt werden. Das Forschungsvorhaben ist motiviert durch Compressed-Sensing (CS) unter zusätzlichen Modellannahmen, und mit Daten, welche nicht-negative Werte haben. Der enorme Erfolg von CS basiert auf der Idee, die intrinsische niedrig-dimensionale Struktur digitaler Signale und Datenmengen für die Rekonstruktion aus sehr wenigen Beobachtungen auszunutzen. Diese Annahmen sind zum Beispiel für Bilder, Videos, Audio-Daten aber auch für viele Muster in Netzwerkdaten und neuere Kodierungsarten in der drahtlosen Kommunikation erfüllt. In bisherigen Ansätzen wird diese Struktur erst nach der Datenerhebung bzw. Abtastung ausgenutzt und das führt in vielen Fällen zu ungünstigen Anforderungen bezüglich Speicherplatz und Bandbreite. Bei CS hingegen wird Datenaufnahme und Kompression in einem Schritt durchgeführt und dadurch eine sofortige und gleichzeitige Unterabtastung ermöglicht. Die intensive Forschung auf diesem Gebiet hat vor allem zu einer Vielzahl von Ergebnissen zu dicht-besetzten Messmatrizen geführt. Dennoch sind für Kommunikations-anwendungen und inverse Netzwerk-Probleme eher strukturierte und schwach-besetzte Matrizen von Bedeutung.  In diesem Projekt sollen deshalb binäre Matrixmodelle untersucht werden, zum Beispiel Matrizen Hadamard-Struktur und vor allem Expander-Matrizen. In diesen Fällen reduziert sich die Rekonstruktion häufig auf sogenannte Sketching-Verfahren, welche bereits in Streamingmethoden und im Graph-Sketching Anwendung finden. Sparsity kann hierbei als eine Struktur erster Ordnung angesehen werden. In der Praxis haben die Daten aber weitere Eigenschaften, folgen zum Beispiel bestimmten Mustermodellen und haben nicht-negative Werte. Diese Eigenschaften haben konkreten Einfluss auf analytische Ansätze und Algorithmen und erfordern zusätzlich Forschung - auch hinsichtlich Algorithmenentwicklung.

Zusatzinformationen / Extras


Schnellnavigation zur Seite über Nummerneingabe