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 .
A.v.H. - Prof. Caire - Alexander von Humboldt Professorship
|A.v.H. - Prof. Caire - Alexander von Humboldt Professorship|
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.
CoSIP - Compressed Sensing and Information Processing - Phase II
|CoSIP - DFG Special
Compressed Sensing and Information Processing
- Phase II|
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
- European Union Horizon 2020 project:|
gan-on-silicon efficient mm-wave european system integration platform 
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
An university cooperation with the African Institute for Mathematical Sciences (AIMS) South Africa, Cameroon, Ghana
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
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
- Berlin Institute for the Foundations of Learning and
The complete BIFOLD project website: $this->_build_link_list($this->linkCount++, "http://bifold.berlin", "bifold.berlin ")