… Abstract—This article investigates beam alignment for multiuser millimeter wave (mmWave)
massive multi-input multioutput system. Unlike the existing works using machine learning (…

… in mm-wave-NOMA systems, we develop a K-meansbased machine learning algorithm for
… Furthermore, to further enhance the performance of the proposed mm-wave-NOMA system, …

… To deal with these issues, we develop a novel machine learning based user association
approach to support multi-connectivity in mmWave networks. Specifically, we first formulate the …

… In this work, we propose a machine learning-based design methodology for multiple
beamforming architectures by mapping each HB architecture to a corresponding neural network. …

… The proposed database can provide a massive number of snapshots for a machine learning
… (Category I), we propose a machine learning-based beam selection algorithm that exploits …

Millimeter wave (mmWave) communication has attracted considerable attention as a key …
BF vectors encourages researchers to leverage relevant machine learning (ML) techniques for …

… beamforming gain to utilize higher bandwidths at millimeter wave (mmWave) and terahertz
(THz) … Consequently, machine learning (ML) algorithms that can identify and learn complex …

… ABSTRACT The use of millimeter wave technologies offer a … supervised and unsupervised
machine learning. In particular, … , various supervised learning approaches are applied to …

Human Activity Recognition (HAR) that includes gait analysis may be useful for various
rehabilitation and telemonitoring applications. Current gait analysis methods, such as wearables …

… By adding a dimension of Machine Learning to existing EM methods, we analyze mm-wave
baluns directly from physical parameters. After training neural networks that accurately learn …