We investigate the performance of multi-user multiple-antenna downlink systems in which a
base station (BS) serves multiple users via a shared wireless medium. In order to fully …

In this paper, we propose a novel deep unsupervised learning-based approach that jointly
optimizes antenna selection and hybrid beamforming to improve the hardware and spectral …

In this paper, a support vector machine (SVM) technique has been applied to an antenna
allocation system with multiple antennas in multiuser downlink communications. Here, only …

This letter studies deep learning (DL) approaches to optimize beamforming vectors in
downlink multi-user multi-antenna systems that can be universally applied to arbitrarily given …

This letter rethinks traditional precoding in multi-user wireless communications with movable
antennas (MAs). Utilizing MAs for optimal antenna positioning, we introduce a sparse …

We consider the downlink of a wireless system where the base-station has M ges 1
antennas and K user terminals have one antenna each. We study the weighted rate sum …

This work revisits the joint beamforming (BF) and antenna selection (AS) problem, as well as
its robust beamforming (RBF) version under imperfect channel state information (CSI). Such …

Large multiple-input multiple-output (MIMO) networks promise high energy efficiency, ie,
much less power is required to achieve the same capacity compared to the conventional …

In the downlink transmission scenario, power allocation and beamforming design at the
transmitter are essential when using multiple antenna arrays. This paper considers a …

Deep learning (DL) techniques have been intensively studied for the optimization of multi-
user multiple-input single-output (MU-MISO) downlink systems owing to the capability of …