Machine learning (ML) which is a subset of artificial intelligence is expected to unlock the
potential of challenging large-scale problems in conventional massive multiple-input …

Many performance gains achieved by massive multiple-input and multiple-output depend on
the accuracy of the downlink channel state information (CSI) at the transmitter (base station) …

The 3rd Generation Partnership Project began studying Release 18 in 2021. Artificial
intelligence (AI)-native air interface is one of the key features of Release 18, where AI for …

We propose a versatile feedback scheme for both single-and multi-user multiple-input
multiple-output (MIMO) frequency division duplex (FDD) systems. Particularly, we propose …

Channel state information (CSI) plays a critical role in achieving the potential benefits of
massive multiple input multiple output (MIMO) systems. In frequency division duplex (FDD) …

Channel state information (CSI) needs to be estimated for reliable and efficient
communication, however, user location information is hidden inside and can be further …

In this work, we use real-world data in order to evaluate and validate a machine learning
(ML)-based algorithm for physical layer functionalities. Specifically, we apply a recently …

Discrete Fourier transform (DFT) codebook-based solutions are well-established for limited
feedback schemes in frequency division duplex (FDD) systems. In recent years, data-aided …

Deep learning (DL) has been increasingly adopted for channel state information (CSI)
feedback to harness the performance gains promised by massive multiple-input multiple …

In this work, we propose two methods that utilize data symbols in addition to pilot symbols for
improved channel estimation quality in a multi-user system, so-called semi-blind channel …