Recently, the ever-increasing demand for bandwidth in multi-modal
commun...
The stringent performance requirements of future wireless networks, such...
As an efficient graph analytical tool, graph neural networks (GNNs) have...
Integrated sensing, computation, and communication (ISCC) has been recen...
As an efficient neural network model for graph data, graph neural networ...
Although semantic communications have exhibited satisfactory performance...
In massive multiple-input multiple-output (MIMO) systems, hybrid
analog-...
Although the semantic communications have exhibited satisfactory perform...
Deep-unfolding neural networks (NNs) have received great attention since...
Integrated sensing and communication (ISAC) has been regarded as one of ...
In this paper, we propose an end-to-end deep learning-based joint transc...
Generative adversarial networks (GANs) are emerging machine learning mod...
Graph neural network (GNN) is an efficient neural network model for grap...
The millimeter wave (mmWave) multiuser multiple-input multiple-output
(M...
The combinatorial auction (CA) is an efficient mechanism for resource
al...
Optimization theory assisted algorithms have received great attention fo...
In cellular federated edge learning (FEEL), multiple edge devices holdin...
Generalized Benders decomposition (GBD) is a globally optimal algorithm ...
It has been a long-held belief that judicious resource allocation is cri...
Link scheduling in device-to-device (D2D) networks is usually formulated...
Training task in classical machine learning models, such as deep neural
...
Resource allocation in wireless networks, such as device-to-device (D2D)...
To coexist with Wi-Fi friendly, a standalone long-term evolution network...
In order to mitigate the long processing delay and high energy consumpti...