Deep learning (DL) has proven its unprecedented success in diverse fields such as
computer vision, natural language processing, and speech recognition by its strong …

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) …

This paper shows that deep neural network (DNN) can be used for efficient and distributed
channel estimation, quantization, feedback, and downlink multiuser precoding for a …

It is anticipated that 6G wireless networks will accelerate the convergence of the physical
and cyber worlds and enable a paradigm-shift in the way we deploy and exploit …

The benefits of end-to-end learning has been demonstrated over AWGN channels but has
not yet been quantified over realistic wireless channel models. This work aims to fill this gap …

Massive multiple-input multiple-output (MIMO) systems require downlink channel state
information (CSI) at the base station (BS) to achieve spatial diversity and multiplexing gains …

The (inverse) discrete Fourier transform (DFT/IDFT) is often perceived as essential to
orthogonal frequency-division multiplexing (OFDM) systems. In this paper, a deep complex …

We introduce a vision transformer (ViT)-based deep joint source and channel coding
(DeepJSCC) scheme for wireless image transmission over multiple-input multiple-output …

Reconfigurable intelligent surface (RIS) enables the configuration of the propagation
environment. Channel estimation is an essential task in realizing the RIS-aided …