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 presents channel-aware adversarial attacks against deep learning-based
wireless signal classifiers. There is a transmitter that transmits signals with different …

In order to fully exploit the advantages of massive multiple-input multiple-output (mMIMO), it
is critical for the transmitter to accurately acquire the channel state information (CSI). Deep …

Multiple-input–multiple-output (MIMO) technology is one of the most widely used
communication technologies. However, with the increasing number of antennas, the …

Recently, the autoencoder framework has shown great potential in reducing the feedback
overhead of the downlink channel state information (CSI). In this work, we further find that …

In this paper, we aim to design an effective learning-based channel state information (CSI)
feedback scheme for the multiple-input multiple-output (MIMO) orthogonal frequency …

The autoencoder empowered by artificial intelligence has shown considerable potential in
solving channel state information (CSI) feedback problems in frequency-division duplexing …

Due to the lack of channel reciprocity in frequency division duplexity (FDD) massive multiple-
input multiple-output (MIMO) systems, it is impossible to infer the downlink channel state …

Abstract Machine learning (ML) has been empowering all aspects of the wireless
communication system design, among which, the reinforcement learning (RL)-based …