A tutorial on ultrareliable and low-latency communications in 6G: Integrating domain knowledge into deep learning
As one of the key communication scenarios in the fifth-generation and also the sixth-
generation (6G) mobile communication networks, ultrareliable and low-latency …
generation (6G) mobile communication networks, ultrareliable and low-latency …
Learning power allocation for multi-cell-multi-user systems with heterogeneous graph neural networks
J Guo, C Yang - IEEE Transactions on Wireless …, 2021 - ieeexplore.ieee.org
A well-trained deep neural network (DNN) enables real-time resource allocation by learning
the relationship between a policy and its impacting parameters. When wireless systems …
the relationship between a policy and its impacting parameters. When wireless systems …
Intelligent trajectory design for secure full-duplex MIMO-UAV relaying against active eavesdroppers: A model-free reinforcement learning approach
MT Mamaghani, Y Hong - IEEE Access, 2020 - ieeexplore.ieee.org
Unmanned aerial vehicle (UAV) assisted wireless communication has recently been
recognized as an inevitably promising component of future wireless networks. Particularly …
recognized as an inevitably promising component of future wireless networks. Particularly …
Unsupervised learning-inspired power control methods for energy-efficient wireless networks over fading channels
Energy-efficiency (EE) is a critical metric within wireless optimization. Power control over
fading channels is considered as a promising EE-improving technique, but requires …
fading channels is considered as a promising EE-improving technique, but requires …
Deep learning aided routing for space-air-ground integrated networks relying on real satellite, flight, and shipping data
Current maritime communications mainly rely on satellites having meager transmission
resources, hence suffering from poorer performance than modern terrestrial wireless …
resources, hence suffering from poorer performance than modern terrestrial wireless …
[HTML][HTML] Artificial intelligence methodologies for data management
This study analyses the main challenges, trends, technological approaches, and artificial
intelligence methods developed by new researchers and professionals in the field of …
intelligence methods developed by new researchers and professionals in the field of …
Regularization strategy aided robust unsupervised learning for wireless resource allocation
Unsupervised learning (UL) is widely used in the wireless resource allocation problems due
to its lower computational complexity and better performance compared with traditional …
to its lower computational complexity and better performance compared with traditional …
Knowledge-driven deep learning paradigms for wireless network optimization in 6g
In the sixth-generation (6G) networks, newly emerging diversified services of massive users
in dynamic network environments are required to be satisfied by multi-dimensional …
in dynamic network environments are required to be satisfied by multi-dimensional …
Deep learning methods for joint optimization of beamforming and fronthaul quantization in cloud radio access networks
Cooperative beamforming across access points (APs) and fronthaul quantization strategies
are essential for cloud radio access network (C-RAN) systems. The nonconvexity of the C …
are essential for cloud radio access network (C-RAN) systems. The nonconvexity of the C …