End-to-end autoencoder (AE) learning has the potential of exceeding the performance of
human-engineered transceivers and encoding schemes, without a priori knowledge of …

Recent advancement in Frequency Domain Duplexing (FDD) enables wireless systems to
use different frequency bands for uplink and downlink communication without explicit …

Using a deep autoencoder (DAE) for end-to-end communication in multiple-input multiple-
output (MIMO) systems is a novel concept with significant potential. DAE-aided MIMO has …

A deep autoencoder (DAE)-based structure for end-to-end communication over the two-user
Z-interference channel (ZIC) with finite-alphabet inputs is designed in this paper. The …

Molecular communication (MC) is a novel paradigm for nano-communication networks.
Compared with diffusion-based single-input single-out (SISO) systems, multiple-input …

In the realm of semantic communication, the significance of encoded features can vary,
while wireless channels are known to exhibit fluctuations across multiple subchannels in …

This article introduces two novel multiple input multiple output spatial division multiplexing
(MIMO-SDM) systems based on deep learning techniques using bit-wise (BW) and symbol …

An end-to-end communications system based on Orthogonal Frequency Division
Multiplexing (OFDM) is modeled as an autoencoder (AE) for which the transmitter (coding …

Abstract Machine learning is a highly promising tool to design the physical layer of wireless
communication systems, but the training usually requires an explicit model of the signal …

In this paper, we study MIMO transmission schemes based on deep learning (DL). We
propose a novel DL-based MIMO communication structure by combing a beamforming …