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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.
CARENET  ContentAware Wireless Networks: Fundamental Limits, Algorithms, and Architectures
CARENET – ContentAware Wireless Networks 

The CARENET project has received funding from European Research Council (ERC) under the European Union with call details Advanced Grants (AdG), PE7, ERC2017ADG.

CoSIP  Compressed Sensing and Information Processing  Phase II
CoSIP  DFG Special Focus Program: 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 secondorder 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 nonToeplitz 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  ganonSilicon Efficient mmwave euRopean systEm iNtegration plAtform
SERENA  European Union Horizon 2020 project: ganonsilicon efficient mmwave european system integration platform 


NonNegative Structured Regression (NonNegative Structured Regression in Communication and Data Science)
NonNegative Structured Regression  DAAD program: SubjectRelated Partnerships with Institutions of Higher Education in Developing Countries An university cooperation with the African Institute for Mathematical Sciences (AIMS) South Africa, Cameroon, Ghana 


SelfOrganizing Complex Networks: A MeanField Game Approach
SelfOrganizing Complex Networks: A MeanField Game Approach  DAAD Programme: Subjectrelated Partnerships with Universities in Developing Countries A university cooperation with the African Institute for Mathematical Sciences (AIMS) South Africa, Cameroon, Ghana 

MeanField theory is a powerful tool to efficiently approximate the behavior of a complex system involving infinitely many agents. In this approximation process, the meanfield replaces the agents' interactions; That is, the average collective effect of the agents becomes the basis of analysis. Meanfield 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 meanfield 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 ultradense 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 