Deep learning has solved many problems that are out of reach of heuristic algorithms. It has
also been successfully applied in wireless communications, even though the current radio …

Wireless communications are envisioned to bring about dramatic changes in the future, with
a variety of emerging applications, such as virtual reality, Internet of Things, and so on …

In this paper, we propose a deep learning (DL) based receiver named comm-transformer
network (Comm-Trans Net), which is robust for different sub-types of tapped delay line (TDL) …

In time-varying fading channels, channel coefficients are estimated using pilot symbols that
are transmitted every coherence interval. For channels with high Doppler spread, the rapid …

Channel state information (CSI), which enables wireless systems to adapt their transmission
parameters to instantaneous channel conditions and consequently achieve great …

When a channel model is available, learning how to communicate on fading noisy channels
can be formulated as the (unsupervised) training of an autoencoder consisting of the …

The evolution of data driven optimization has been shown advantageous in many
applications. In this paper, we propose a deep learning architecture for the wireless …

For estimating the MIMO channels for both the uplink and the downlink, we used residual
deep neural network. The proposed residual deep neural network (ResNET) channel …

This paper proposes a generative adversarial network (GAN) based channel estimation
scheme for intelligent reflecting surface (IRS)-aided single-input multiple-output (SIMO) …

When a channel model is not available, the end-to-end training of encoder and decoder on
a fading noisy channel generally requires the repeated use of the channel and of a feedback …