Numerical evidence suggests that compressive sensing (CS) approaches for wideband
massive MIMO channel estimation can achieve very good performance with limited training …

Advanced multiple input multiple output (MIMO) technologies in New Radio (NR), such as
multiple transmit-receive-point (multi-TRP) transmission or non-linear precoding are …

We study the problem of providing channel state information (CSI) at the transmitter in multi-
user “massive” MIMO systems operating in frequency division duplexing (FDD). The …

Millimeter-wave massive multiple-input multiple-output employing a large-scale antenna
array is a promising technology for 5G and 6G cellular networks. It also provides strong …

We propose and analyze a solution to the problem of recovering a block sparse signal with
sparse blocks from linear measurements. Such problems naturally emerge inter alia in the …

ONE5G (E2E-aware Optimizations and advancements for the Network Edge of 5G New
Radio) is an European funded collaborative project, aiming at designing Radio Access …

Hierarchically sparse signals and Kronecker product structured measurements naturally
arise in a variety of applications. The simplest example of a hierarchical sparsity structure is …

In this work, we propose a compressed sensing framework for radar parameter estimation in
an Integrated Sensing and Communication (ISAC) system employing a realistic and …

Extracting tomographic information about quantum states is a crucial task in the quest
towards devising high-precision quantum devices. Current schemes typically require …

In this work, we propose a framework based on Deep Neural Networks (DNNs) for radar
parameter estimation in an Integrated Sensing and Communication (ISAC) system em …