Online deep reinforcement learning for computation offloading in blockchain-empowered mobile edge computing
Offloading computation-intensive tasks (eg, blockchain consensus processes and data
processing tasks) to the edge/cloud is a promising solution for blockchain-empowered …
processing tasks) to the edge/cloud is a promising solution for blockchain-empowered …
LightAMC: Lightweight automatic modulation classification via deep learning and compressive sensing
Automatic modulation classification (AMC) is an promising technology for non-cooperative
communication systems in both military and civilian scenarios. Recently, deep learning (DL) …
communication systems in both military and civilian scenarios. Recently, deep learning (DL) …
Deep reinforcement learning and permissioned blockchain for content caching in vehicular edge computing and networks
Y Dai, D Xu, K Zhang, S Maharjan… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Vehicular Edge Computing (VEC) is a promising paradigm to enable huge amount of data
and multimedia content to be cached in proximity to vehicles. However, high mobility of …
and multimedia content to be cached in proximity to vehicles. However, high mobility of …
Path planning for UAV-mounted mobile edge computing with deep reinforcement learning
In this letter, we study an unmanned aerial vehicle (UAV)-mounted mobile edge computing
network, where the UAV executes computational tasks offloaded from mobile terminal users …
network, where the UAV executes computational tasks offloaded from mobile terminal users …
Deep reinforcement learning for dynamic uplink/downlink resource allocation in high mobility 5G HetNet
Recently, the 5G is widely deployed for supporting communications of high mobility nodes
including train, vehicular and unmanned aerial vehicles (UAVs) largely emerged as the …
including train, vehicular and unmanned aerial vehicles (UAVs) largely emerged as the …
Cooperative offloading and resource management for UAV-enabled mobile edge computing in power IoT system
Y Liu, S Xie, Y Zhang - IEEE Transactions on Vehicular …, 2020 - ieeexplore.ieee.org
The lack of the computation services in remote areas motivates power Internet of Things
(IoT) to apply unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) …
(IoT) to apply unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) …
Survey on device to device (D2D) communication for 5GB/6G networks: Concept, applications, challenges, and future directions
Device-to-device (D2D) communication is one of the most promising technologies in
wireless cellular networks that can be employed to improve spectral and energy efficiency …
wireless cellular networks that can be employed to improve spectral and energy efficiency …
Joint resources and workflow scheduling in UAV-enabled wirelessly-powered MEC for IoT systems
This paper considers a UAV-enabled mobile edge computing (MEC) system, where a UAV
first powers the Internet of things device (IoTD) by utilizing Wireless Power Transfer (WPT) …
first powers the Internet of things device (IoTD) by utilizing Wireless Power Transfer (WPT) …
An intelligent traffic load prediction-based adaptive channel assignment algorithm in SDN-IoT: A deep learning approach
Due to the fast increase of sensing data and quick response requirement in the Internet of
Things (IoT) delivery network, the high speed transmission has emerged as an important …
Things (IoT) delivery network, the high speed transmission has emerged as an important …
A deep reinforcement learning-based dynamic traffic offloading in space-air-ground integrated networks (SAGIN)
Space-Air-Ground Integrated Networks (SAGIN) is considered as the key structure of the
next generation network. The space satellites and air nodes are the potential candidates to …
next generation network. The space satellites and air nodes are the potential candidates to …