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20172026
most citedOn the linear convergence rates of exchange and continuous methods for total variation minimization

33 citations · 38 across the 11 of their papers we have counts for

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cs.IT20242 cited

Perfectly Secure Key Agreement Over a Full Duplex Wireless Channel

Gerhard Wunder, Axel Flinth, Daniel Becker +1

Secret key generation (SKG) between authenticated devices is a pivotal task for secure communications. Diffie-Hellman (DH) is de-facto standard but not post-quantum secure. In this…

cs.IT2021

Guaranteed blind deconvolution and demixing via hierarchically sparse reconstruction

Axel Flinth, Ingo Roth, Benedikt Groß +2

The blind deconvolution problem amounts to reconstructing both a signal and a filter from the convolution of these two. It constitutes a prominent topic in mathematical and enginee…

cs.IT2021

Measure Concentration on the OFDM-based Random Access Channel

Gerhard Wunder, Axel Flinth, Benedikt Groß

It is well known that CS can boost massive random access protocols. Usually, the protocols operate in some overloaded regime where the sparsity can be exploited. In this paper, we…

cs.IT2021

Hierarchical sparse recovery from hierarchically structured measurements with application to massive random access

Benedikt Groß, Axel Flinth, Ingo Roth +2

A new family of operators, coined hierarchical measurement operators, is introduced and discussed within the well-known hierarchical sparse recovery framework. Such operator is a c…

cs.IT2018

Low-Overhead Hierarchically-Sparse Channel Estimation for Multiuser Wideband Massive MIMO

Gerhard Wunder, Stelios Stefanatos, Axel Flinth +2

The problem of excessive pilot overhead required for uplink massive MIMO channel estimation is well known, let alone when it is considered along with wideband (OFDM) transmissions.…

cs.IT2018

Hierarchical Sparse Channel Estimation for Massive MIMO

Gerhard Wunder, Ingo Roth, Axel Flinth +4

The problem of wideband massive MIMO channel estimation is considered. Targeting for low complexity algorithms as well as small training overhead, a compressive sensing (CS) approa…