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TU Berlin

Inhalt des Dokuments

Aktuelle Forschungsprojekte

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

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.



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