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Communications and Information Theory ChairCurrent Projects

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Research by the CommIT chair

The Communications and Information Theory Chair (CommIT) focuses on fundamental and applied research on communication theory, information theory, signal processing, and networks, with a particular emphasis on wireless communication systems. The chair successfully proposed multiple funded research projects within these fields. Some of the publicly funded and currently running projects are listed below. In addition, the chair collaborates with industrial partners on multiple topics.

Beside the more theoretical research, the chair developed and runs different testbeds and prototypes. A selection can be found under testbeds and prototypes.

Completed projects can be found here.

Current research projects

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 -- 2020
  • Title: "Foundations and Architectures for the Next Generation of Wireless Networks''
  • Progress reports201420152016, 2017, 2018, 2019

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 - 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 program: Subject-Related Partnerships with Institutions of Higher Education in Developing Countries

An university cooperation with the African Institute for Mathematical Sciences (AIMS) South Africa, Cameroon, Ghana
  • co-PI: G. Caire, P. Jung
  • budget: EUR 200,000
  • duration: 01/02/2018 - 31/01/2022
  • title: Non-Negative Structured Regression with Applications in Communication and Data Science
  • project description: In this project we propose to design efficient algorithms for the reconstruction of redundant-encoded signals in wireless communication and network properties in data science. The main motivation of this line of work comes from model-based compressed sensing (CS) with non-negativity priors. CS is based on the fact that the intrinsic dimension of many digital signals or large data sets is typically far less than their ambient dimensions, for example the sparse representation of images, videos, audio data, network status information like activity and novel coding techniques for wireless communication. Traditionally, redundancy and structure in the data is exploited after the acquisition (measurement), which may be very costly in terms storage and bandwidth. CS instead attempts to overcome this by performing sampling and compression simultaneously, i.e., acquisition from a sub-Nyquist perspective. CS works well with provable guarantees for dense matrices. However, in communication engineering and data science problems related to complex networks structured sparse matrices are used due to more efficient storage and processing. Therefore we focus also on binary and sparse matrices, formed for example by sampling Hadamard matrices, expander matrices, etc. For such type of matrices CS often reduces to linear sketching, which has been applied in data streaming, and graph sketching. Furthermore, sparsity and compressibility can be regarded as first order structure of signals and objects of interest. In practice, most objects of interest exhibit second other structures like block-sparsity, tree-sparsity, non-negativity, etc. Both, the communication and complex networks problems we consider have often a conic constraint like non-negativity, hence the title of the project.

Self-Organizing Complex Networks: A Mean-Field Game Approach

Self-Organizing Complex Networks: A Mean-Field Game Approach - DAAD Programme: Subject-related Partnerships with Universities in Developing Countries

A university cooperation with the African Institute for Mathematical Sciences (AIMS) South Africa, Cameroon, Ghana


  • co-PI: G. Caire, P. Jung, S. Maghsudi
  • budget: EUR 600,000
  • duration: 01/09/2019 - 30/06/2022
  • title: Self-Organizing Complex Networks: A Mean-Field Evolutionary Game Theoretic Approach 
  • project description
Mean-Field theory is a powerful tool to efficiently approximate the behavior of a complex system involving infinitely many agents. In this approximation process, the mean-field replaces the agents' interactions; That is, the average collective effect of the agents becomes the basis of analysis. Mean-field theory finds applications in several fields and in recent years, it has gained popularity in game theory, artificial intelligence, and engineering. 
Furthermore, the theory of Optimal transport has deep connections with recent several research fields, e.g., efficient resource allocation in wireless communications and also domain adaptation in learning and trained algorithms. It stands as a powerful tool to study flows and analyze energy functionals on the space of probability measures. The theory has also attracted the attention of communication society to address the several problems that arise in wireless networks.
In this project, the goal is to analyze complex systems using the mean-field and transport theory in different settings, for example, when the agents have different types, or when the communication between the agents is constrained and limited. The theoretical results are then applied to optimize the ultra-dense wireless communication networks. 

BIFOLD - Berlin Institute for the Foundations of Learning and Data

BIFOLD - Berlin Institute for the Foundations of Learning and Data

The complete BIFOLD project website: bifold.berlin


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