Applications of artificial intelligence and machine learning in smart cities
Smart cities are aimed to efficiently manage growing urbanization, energy consumption,
maintain a green environment, improve the economic and living standards of their citizens …
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
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 …
speed exacerbated by the proliferation of billions of data-intensive applications. To address …
AI for UAV-assisted IoT applications: A comprehensive review
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 …
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
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 …
architecture featuring open, software-driven, virtual, and intelligent radio access architecture …
LightAMC: Lightweight automatic modulation classification via deep learning and compressive sensing
Automatic modulation classification (AMC) is an promising technology for non-cooperative
communication systems in both military and civilian scenarios. Recently, deep learning (DL) …
communication systems in both military and civilian scenarios. Recently, deep learning (DL) …
Fast beamforming design via deep learning
H Huang, Y Peng, J Yang, W **a… - 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 …
multiple-input and multiple-output (MIMO) systems. Among existing design criterions, sum …
Deep reinforcement learning for dynamic uplink/downlink resource allocation in high mobility 5G HetNet
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 …
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 …
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
This paper proposes a model-driven deep learning (MDDL)-based channel estimation and
feedback scheme for wideband millimeter-wave (mmWave) massive hybrid multiple-input …
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
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 …
technique that can be utilized to provide a fully data-driven network evaluating and …