Machine learning for large-scale optimization in 6g wireless networks
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
[HTML][HTML] Joint flying relay location and routing optimization for 6g uav–iot networks: A graph neural network-based approach
Unmanned aerial vehicles (UAVs) are widely used in Internet-of-Things (IoT) networks,
especially in remote areas where communication infrastructure is unavailable, due to …
especially in remote areas where communication infrastructure is unavailable, due to …
Deep learning for energy efficient beamforming in MU-MISO networks: A GAT-based approach
This letter investigates the deep learning enabled energy efficient beamforming design for
multi-user (MU) multiple-input single-output (MISO) networks. An energy efficiency (EE) …
multi-user (MU) multiple-input single-output (MISO) networks. An energy efficiency (EE) …
Message passing meets graph neural networks: A new paradigm for massive MIMO systems
As one of the core technologies for 5G systems, massive multiple-input multiple-output
(MIMO) introduces dramatic capacity improvements along with very high beamforming and …
(MIMO) introduces dramatic capacity improvements along with very high beamforming and …
From Graph Theory to Graph Neural Networks (GNNs): The Opportunities of GNNs in Power Electronics
Graph theory within power electronics, developed over a 50-year span, is continually
evolving, necessitating ongoing research endeavors. Facing with the never-been-seen …
evolving, necessitating ongoing research endeavors. Facing with the never-been-seen …
AI-native transceiver design for near-field ultra-massive MIMO: Principles and techniques
Ultra-massive multiple-input multiple-output (UMMIMO) is a cutting-edge technology that
promises to revolutionize wireless networks by providing an unprecedentedly high spectral …
promises to revolutionize wireless networks by providing an unprecedentedly high spectral …
Blind performance prediction for deep learning based ultra-massive MIMO channel estimation
Reliability is of paramount importance for the physical layer of wireless systems due to its
decisive impact on end-to-end performance. However, the uncertainty of prevailing deep …
decisive impact on end-to-end performance. However, the uncertainty of prevailing deep …
GNN-Enabled Max-Min Fair Beamforming
This paper investigates a graph neural network (GNN)-enabled beamforming design to
achieve max-min fairness for multi-user multiple-input-single-output (MU-MISO) networks …
achieve max-min fairness for multi-user multiple-input-single-output (MU-MISO) networks …
Understanding the performance of learning precoding policy with GNN and CNNs
Learning-based precoding has been shown able to be implemented in real-time, jointly
optimized with channel acquisition, and robust to imperfect channels. Yet previous works …
optimized with channel acquisition, and robust to imperfect channels. Yet previous works …
ENGNN: A general edge-update empowered GNN architecture for radio resource management in wireless networks
In order to achieve high data rate and ubiquitous connectivity in future wireless networks, a
key task is to efficiently manage the radio resource by judicious beamforming and power …
key task is to efficiently manage the radio resource by judicious beamforming and power …