The next wave of wireless technologies is proliferating in connecting things among
themselves as well as to humans. In the era of the Internet of Things (IoT), billions of …

Ultradense cell-free massive multiple-input–multiple-output (CF-MMIMO) has emerged as a
promising technology expected to meet the future ubiquitous connectivity requirements and …

Recent work in machine learning shows that deep neural networks can be used to solve a
wide variety of inverse problems arising in computational imaging. We explore the central …

Deep neural networks provide unprecedented performance gains in many real-world
problems in signal and image processing. Despite these gains, the future development and …

We study federated machine learning at the wireless network edge, where limited power
wireless devices, each with its own dataset, build a joint model with the help of a remote …

Reconfigurable intelligent surfaces (RISs) are regarded as a promising emerging hardware
technology to improve the spectrum and energy efficiency of wireless networks by artificially …

We study federated machine learning (ML) at the wireless edge, where power-and
bandwidth-limited wireless devices with local datasets carry out distributed stochastic …

Since their inception in the 1930–1960s, the research disciplines of computational imaging
and machine learning have followed parallel tracks and, during the last two decades …

In frequency division duplex mode, the downlink channel state information (CSI) should be
sent to the base station through feedback links so that the potential gains of a massive …

In this paper, we investigate the model-driven deep learning (DL) for MIMO detection. In
particular, the MIMO detector is specially designed by unfolding an iterative algorithm and …