Edge computing with artificial intelligence: A machine learning perspective
Recent years have witnessed the widespread popularity of Internet of things (IoT). By
providing sufficient data for model training and inference, IoT has promoted the development …
providing sufficient data for model training and inference, IoT has promoted the development …
Convergence of edge computing and deep learning: A comprehensive survey
Ubiquitous sensors and smart devices from factories and communities are generating
massive amounts of data, and ever-increasing computing power is driving the core of …
massive amounts of data, and ever-increasing computing power is driving the core of …
Multi-agent reinforcement learning based resource management in MEC-and UAV-assisted vehicular networks
In this paper, we investigate multi-dimensional resource management for unmanned aerial
vehicles (UAVs) assisted vehicular networks. To efficiently provide on-demand resource …
vehicles (UAVs) assisted vehicular networks. To efficiently provide on-demand resource …
A survey of multi-access edge computing in 5G and beyond: Fundamentals, technology integration, and state-of-the-art
Driven by the emergence of new compute-intensive applications and the vision of the
Internet of Things (IoT), it is foreseen that the emerging 5G network will face an …
Internet of Things (IoT), it is foreseen that the emerging 5G network will face an …
Toward edge intelligence: Multiaccess edge computing for 5G and Internet of Things
To satisfy the increasing demand of mobile data traffic and meet the stringent requirements
of the emerging Internet-of-Things (IoT) applications such as smart city, healthcare, and …
of the emerging Internet-of-Things (IoT) applications such as smart city, healthcare, and …
Deep reinforcement learning for Internet of Things: A comprehensive survey
The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in
communication, computing, caching and control (4Cs) problems. The recent advances in …
communication, computing, caching and control (4Cs) problems. The recent advances in …
Artificial neural networks-based machine learning for wireless networks: A tutorial
In order to effectively provide ultra reliable low latency communications and pervasive
connectivity for Internet of Things (IoT) devices, next-generation wireless networks can …
connectivity for Internet of Things (IoT) devices, next-generation wireless networks can …
Lyapunov-guided deep reinforcement learning for stable online computation offloading in mobile-edge computing networks
Opportunistic computation offloading is an effective method to improve the computation
performance of mobile-edge computing (MEC) networks under dynamic edge environment …
performance of mobile-edge computing (MEC) networks under dynamic edge environment …
AI-assisted network-slicing based next-generation wireless networks
The integration of communications with different scales, diverse radio access technologies,
and various network resources renders next-generation wireless networks (NGWNs) highly …
and various network resources renders next-generation wireless networks (NGWNs) highly …
Blockchain and machine learning for communications and networking systems
Recently, with the rapid development of information and communication technologies, the
infrastructures, resources, end devices, and applications in communications and networking …
infrastructures, resources, end devices, and applications in communications and networking …