Novel design of user scheduling and analog beam selection in downlink millimeter-wave communications

Z Zou, S Zhao, G Huang, D Tang - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
In this article, the joint design for user scheduling and analog beam selection in a downlink
multiuser millimeter-wave (mmWave) system is studied. Our objective is to maximize the …

Machine learning for 5G/B5G mobile and wireless communications: Potential, limitations, and future directions

ME Morocho-Cayamcela, H Lee, W Lim - IEEE access, 2019 - ieeexplore.ieee.org
Driven by the demand to accommodate today's growing mobile traffic, 5G is designed to be
a key enabler and a leading infrastructure provider in the information and communication …

Self-supervised deep learning for mmWave beam steering exploiting sub-6 GHz channels

I Chafaa, R Negrel, EV Belmega… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
mmWave communication requires accurate and continuous beam steering to overcome the
severe propagation loss and user mobility. In this paper, we leverage a self-supervised deep …

Joint SDMA and power-domain NOMA system for multi-user mm-wave communications

I Khaled, C Langlais, A El Falou… - 2020 International …, 2020 - ieeexplore.ieee.org
Digital beamsteering (DBS) is an appealing technique to overcome the severe millimeter-
wave path loss, with less complexity and less channel feedback. In multi-user systems, DBS …

FusionNet: Enhanced beam prediction for mmWave communications using sub-6 GHz channel and a few pilots

F Gao, B Lin, C Bian, T Zhou, J Qian… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
In order to reduce the downlink training overhead of mmWave communications, we propose
a novel downlink beamforming strategy using the uplink sub-6GHz channel and downlink …

Fast learning for dynamic resource allocation in AI-enabled radio networks

MA Qureshi, C Tekin - IEEE Transactions on Cognitive …, 2019 - ieeexplore.ieee.org
Artificial Intelligence (AI)-enabled radios are expected to enhance the spectral efficiency of
5th generation (5G) millimeter wave (mmWave) networks by learning to optimize network …

Deep reinforcement learning-based coordinated beamforming for mmWave massive MIMO vehicular networks

P Tarafder, W Choi - Sensors, 2023 - mdpi.com
As a critical enabler for beyond fifth-generation (B5G) technology, millimeter wave
(mmWave) beamforming for mmWave has been studied for many years. Multi-input multi …

Learning-based load balancing handover in mobile millimeter wave networks

S Khosravi, HS Ghadikolaei… - GLOBECOM 2020-2020 …, 2020 - ieeexplore.ieee.org
Millimeter-wave (mmWave) communication is a promising solution to the high data rate
demands in the upcoming 5G and beyond communication networks. When it comes to …

MAMBA: A multi-armed bandit framework for beam tracking in millimeter-wave systems

I Aykin, B Akgun, M Feng… - IEEE INFOCOM 2020-IEEE …, 2020 - ieeexplore.ieee.org
Millimeter-wave (mmW) spectrum is a major candidate to support the high data rates of 5G
systems. However, due to directionality of mmW communication systems, misalignments …

Self-organizing mm wave networks: A power allocation scheme based on machine learning

R Amiri, H Mehrpouyan - 2018 11th Global symposium on …, 2018 - ieeexplore.ieee.org
Millimeter-wave (mmWave) communication is anticipated to provide significant throughout
gains in urban scenarios. To this end, network densification is a necessity to meet the high …