6G for vehicle-to-everything (V2X) communications: Enabling technologies, challenges, and opportunities

M Noor-A-Rahim, Z Liu, H Lee, MO Khyam… - Proceedings of the …, 2022 - ieeexplore.ieee.org
We are on the cusp of a new era of connected autonomous vehicles with unprecedented
user experiences, tremendously improved road safety and air quality, highly diverse …

Quantum machine learning for 6G communication networks: State-of-the-art and vision for the future

SJ Nawaz, SK Sharma, S Wyne, MN Patwary… - IEEE …, 2019 - ieeexplore.ieee.org
The upcoming fifth generation (5G) of wireless networks is expected to lay a foundation of
intelligent networks with the provision of some isolated artificial intelligence (AI) operations …

Graph neural networks for scalable radio resource management: Architecture design and theoretical analysis

Y Shen, Y Shi, J Zhang… - IEEE Journal on Selected …, 2020 - ieeexplore.ieee.org
Deep learning has recently emerged as a disruptive technology to solve challenging radio
resource management problems in wireless networks. However, the neural network …

Overview of deep learning-based CSI feedback in massive MIMO systems

J Guo, CK Wen, S Jin, GY Li - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Many performance gains achieved by massive multiple-input and multiple-output depend on
the accuracy of the downlink channel state information (CSI) at the transmitter (base station) …

6G white paper on machine learning in wireless communication networks

S Ali, W Saad, N Rajatheva, K Chang… - arXiv preprint arXiv …, 2020 - arxiv.org
The focus of this white paper is on machine learning (ML) in wireless communications. 6G
wireless communication networks will be the backbone of the digital transformation of …

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 …

Few-shot specific emitter identification via deep metric ensemble learning

Y Wang, G Gui, Y Lin, HC Wu, C Yuen… - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
Specific emitter identification (SEI) is a highly potential technology for physical-layer
authentication that is one of the most critical supplements for the upper-layer authentication …

Deep reinforcement learning for 5G networks: Joint beamforming, power control, and interference coordination

FB Mismar, BL Evans… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
The fifth generation of wireless communications (5G) promises massive increases in traffic
volume and data rates, as well as improved reliability in voice calls. Jointly optimizing …

Multi-branch deep residual learning for clustering and beamforming in user-centric network

Y He, L Dai, H Zhang - IEEE communications letters, 2020 - ieeexplore.ieee.org
In existing works of deep learning-based resource allocation, the scalability degrades
heavily with the increase of network complexity, which is due to their limited learning ability …

Deep learning-based CSI feedback for beamforming in single-and multi-cell massive MIMO systems

J Guo, CK Wen, S Jin - IEEE Journal on Selected Areas in …, 2020 - ieeexplore.ieee.org
The potentials of massive multiple-input multiple-output (MIMO) are all based on the
available instantaneous channel state information (CSI) at the base station (BS). Therefore …