Applications of artificial intelligence and machine learning in smart cities

Z Ullah, F Al-Turjman, L Mostarda… - Computer Communications, 2020 - Elsevier
Smart cities are aimed to efficiently manage growing urbanization, energy consumption,
maintain a green environment, improve the economic and living standards of their citizens …

6G enabled smart infrastructure for sustainable society: Opportunities, challenges, and research roadmap

AL Imoize, O Adedeji, N Tandiya, S Shetty - Sensors, 2021 - mdpi.com
The 5G wireless communication network is currently faced with the challenge of limited data
speed exacerbated by the proliferation of billions of data-intensive applications. To address …

AI for UAV-assisted IoT applications: A comprehensive review

N Cheng, S Wu, X Wang, Z Yin, C Li… - IEEE Internet of …, 2023 - ieeexplore.ieee.org
With the rapid development of the Internet of Things (IoT), there are a dramatically
increasing number of devices, leading to the fact that only using terrestrial infrastructure can …

Deep learning for B5G open radio access network: Evolution, survey, case studies, and challenges

B Brik, K Boutiba, A Ksentini - IEEE Open Journal of the …, 2022 - ieeexplore.ieee.org
Open Radio Access Network (O-RAN) alliance was recently launched to devise a new RAN
architecture featuring open, software-driven, virtual, and intelligent radio access architecture …

Fast beamforming design via deep learning

H Huang, Y Peng, J Yang, W Xia… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Beamforming is considered as one of the most important techniques for designing advanced
multiple-input and multiple-output (MIMO) systems. Among existing design criterions, sum …

LightAMC: Lightweight automatic modulation classification via deep learning and compressive sensing

Y Wang, J Yang, M Liu, G Gui - IEEE Transactions on Vehicular …, 2020 - ieeexplore.ieee.org
Automatic modulation classification (AMC) is an promising technology for non-cooperative
communication systems in both military and civilian scenarios. Recently, deep learning (DL) …

Deep reinforcement learning for dynamic uplink/downlink resource allocation in high mobility 5G HetNet

F Tang, Y Zhou, N Kato - IEEE Journal on selected areas in …, 2020 - ieeexplore.ieee.org
Recently, the 5G is widely deployed for supporting communications of high mobility nodes
including train, vehicular and unmanned aerial vehicles (UAVs) largely emerged as the …

Towards artificial intelligence enabled 6G: State of the art, challenges, and opportunities

S Zhang, D Zhu - Computer Networks, 2020 - Elsevier
Abstract 6G is expected to support the unprecedented Internet of everything scenarios with
extremely diverse and challenging requirements. To fulfill such diverse requirements …

Model-driven deep learning based channel estimation and feedback for millimeter-wave massive hybrid MIMO systems

X Ma, Z Gao, F Gao, M Di Renzo - IEEE Journal on Selected …, 2021 - ieeexplore.ieee.org
This paper proposes a model-driven deep learning (MDDL)-based channel estimation and
feedback scheme for wideband millimeter-wave (mmWave) massive hybrid multiple-input …

A survey on deep learning for ultra-reliable and low-latency communications challenges on 6G wireless systems

A Salh, L Audah, NSM Shah, A Alhammadi… - IEEE …, 2021 - ieeexplore.ieee.org
The sixth generation (6G) wireless communication network presents itself as a promising
technique that can be utilized to provide a fully data-driven network evaluating and …