Towards 6G wireless communication networks: Vision, enabling technologies, and new paradigm shifts
The fifth generation (5G) wireless communication networks are being deployed worldwide
from 2020 and more capabilities are in the process of being standardized, such as mass …
from 2020 and more capabilities are in the process of being standardized, such as mass …
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 …
Mobility-aware multi-hop task offloading for autonomous driving in vehicular edge computing and networks
Vehicular Edge Computing (VEC) has gained increasing interest due to its potential to
provide low latency and reduce the load in backhaul networks. In order to meet drastically …
provide low latency and reduce the load in backhaul networks. In order to meet drastically …
Future intelligent and secure vehicular network toward 6G: Machine-learning approaches
As a powerful tool, the vehicular network has been built to connect human communication
and transportation around the world for many years to come. However, with the rapid growth …
and transportation around the world for many years to come. However, with the rapid growth …
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 …
Deep reinforcement learning for energy-efficient computation offloading in mobile-edge computing
Mobile-edge computing (MEC) has emerged as a promising computing paradigm in the 5G
architecture, which can empower user equipments (UEs) with computation and energy …
architecture, which can empower user equipments (UEs) with computation and energy …
When deep reinforcement learning meets federated learning: Intelligent multitimescale resource management for multiaccess edge computing in 5G ultradense …
Recently, smart cities, healthcare system, and smart vehicles have raised challenges on the
capability and connectivity of state-of-the-art Internet-of-Things (IoT) devices, especially for …
capability and connectivity of state-of-the-art Internet-of-Things (IoT) devices, especially for …
A survey on the computation offloading approaches in mobile edge computing: A machine learning-based perspective
A Shakarami, M Ghobaei-Arani, A Shahidinejad - Computer Networks, 2020 - Elsevier
With the rapid developments in emerging mobile technologies, utilizing resource-hungry
mobile applications such as media processing, online Gaming, Augmented Reality (AR) …
mobile applications such as media processing, online Gaming, Augmented Reality (AR) …
Applications of deep reinforcement learning in communications and networking: A survey
This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
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 …