Deep reinforcement learning for autonomous internet of things: Model, applications and challenges
The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices
around the world, where the IoT devices collect and share information to reflect status of the …
around the world, where the IoT devices collect and share information to reflect status of the …
A comprehensive survey on clustering in vehicular networks: Current solutions and future challenges
Vehicular networks are on the verge of deployment, thanks to the advancements in
computation and communication technologies. This breed of ad hoc networks leverages …
computation and communication technologies. This breed of ad hoc networks leverages …
Deep reinforcement learning based resource allocation for V2V communications
In this paper, we develop a novel decentralized resource allocation mechanism for vehicle-
to-vehicle (V2V) communications based on deep reinforcement learning, which can be …
to-vehicle (V2V) communications based on deep reinforcement learning, which can be …
Intelligent network data analytics function in 5G cellular networks using machine learning
S Sevgican, M Turan, K Gökarslan… - Journal of …, 2020 - ieeexplore.ieee.org
5G cellular networks come with many new features compared to the legacy cellular
networks, such as network data analytics function (NWDAF), which enables the network …
networks, such as network data analytics function (NWDAF), which enables the network …
Comprehensive survey of machine learning approaches in cognitive radio-based vehicular ad hoc networks
Nowadays, machine learning (ML), which is one of the most rapidly growing technical tools,
is extensively used to solve critical challenges in various domains. Vehicular ad hoc network …
is extensively used to solve critical challenges in various domains. Vehicular ad hoc network …
Network slicing based learning techniques for iov in 5g and beyond networks
The effects of transport development on people's lives are diverse, ranging from economy to
tourism, health care, etc. Great progress has been made in this area, which has led to the …
tourism, health care, etc. Great progress has been made in this area, which has led to the …
A survey on multi-agent reinforcement learning methods for vehicular networks
I Althamary, CW Huang, P Lin - 2019 15th International …, 2019 - ieeexplore.ieee.org
Under the rapid development of the Internet of Things (IoT), vehicles can be recognized as
mobile smart agents that communicating, cooperating, and competing for resources and …
mobile smart agents that communicating, cooperating, and competing for resources and …
Deep reinforcement learning based distributed resource allocation for V2V broadcasting
In this article, we exploit deep reinforcement learning for joint resource allocation and
scheduling in vehicle-to-vehicle (V2V) broadcast communications. Each vehicle, considered …
scheduling in vehicle-to-vehicle (V2V) broadcast communications. Each vehicle, considered …
Deep learning enabled beam tracking for non-line of sight millimeter wave communications
To solve the complex beam alignment issue in non-line-of-sight (NLOS) millimeter wave
communications, this paper presents a deep neural network (DNN) based procedure to …
communications, this paper presents a deep neural network (DNN) based procedure to …
Computation offloading algorithms in vehicular edge computing environment: A survey
S Talal, WSM Yousef… - … Conference on Intelligent …, 2021 - ieeexplore.ieee.org
Vehicular Edge Computing (VEC) is a promising paradigm that approximates cloud services
near vehicles with the assistance of offloading. Data and task offloading have aided vehicles …
near vehicles with the assistance of offloading. Data and task offloading have aided vehicles …