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 …
AoI-aware energy control and computation offloading for industrial IoT
Abstract In Industrial Internet of Things (IIoT), a large volume of data is collected periodically
by IoT devices, and timely data routing and processing are important requirements. Age of …
by IoT devices, and timely data routing and processing are important requirements. Age of …
Joint computation offloading and resource allocation for edge-cloud collaboration in internet of vehicles via deep reinforcement learning
Mobile edge computing (MEC) and cloud computing (CC) have been considered as the key
technologies to improve the task processing efficiency for Internet of Vehicles (IoV). In this …
technologies to improve the task processing efficiency for Internet of Vehicles (IoV). In this …
Adaptive transmission scheduling in wireless networks for asynchronous federated learning
In this paper, we study asynchronous federated learning (FL) in a wireless distributed
learning network (WDLN). To allow each edge device to use its local data more efficiently …
learning network (WDLN). To allow each edge device to use its local data more efficiently …
Reinforcement learning approaches for efficient and secure blockchain-powered smart health systems
Emerging technological innovation toward e-Health transition is a worldwide priority for
ensuring people's quality of life. Hence, secure exchange and analysis of medical data …
ensuring people's quality of life. Hence, secure exchange and analysis of medical data …
Collaborative policy learning for dynamic scheduling tasks in cloud-edge-terminal IoT networks using federated reinforcement learning
In this article, we examine cloud–edge–terminal Internet of Things (IoT) networks, where
edges undertake a range of typical dynamic scheduling tasks. In these IoT networks, a …
edges undertake a range of typical dynamic scheduling tasks. In these IoT networks, a …
Deep learning-based resource allocation for device-to-device communication
In this paper, a deep learning (DL) framework for the optimization of the resource allocation
in multi-channel cellular systems with device-to-device (D2D) communication is proposed …
in multi-channel cellular systems with device-to-device (D2D) communication is proposed …
Meta-scheduling framework with cooperative learning toward beyond 5G
In this paper, we propose a novel meta-scheduling framework with cooperative learning that
fully exploits a functional split structure of the base station (BS) consisting of a central unit …
fully exploits a functional split structure of the base station (BS) consisting of a central unit …
Radio and energy resource management in renewable energy-powered wireless networks with deep reinforcement learning
In this paper, we study radio and energy resource management in renewable energy-
powered wireless networks, where base stations (BSs) are powered by both on-grid and …
powered wireless networks, where base stations (BSs) are powered by both on-grid and …
Reinforcement learning meets wireless networks: A layering perspective
Driven by the soaring traffic demand and the growing diversity of mobile services, wireless
networks are evolving to be increasingly dense and heterogeneous. Accordingly, in such …
networks are evolving to be increasingly dense and heterogeneous. Accordingly, in such …