Federated deep reinforcement learning for the distributed control of NextG wireless networks
P Tehrani, F Restuccia… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
Next Generation (NextG) networks are expected to support demanding tactile internet
applications such as augmented reality and connected autonomous vehicles. Whereas …
applications such as augmented reality and connected autonomous vehicles. Whereas …
Deep reinforcement learning for mobile 5G and beyond: Fundamentals, applications, and challenges
Future-generation wireless networks (5G and beyond) must accommodate surging growth in
mobile data traffic and support an increasingly high density of mobile users involving a …
mobile data traffic and support an increasingly high density of mobile users involving a …
Deep reinforcement learning based wireless network optimization: A comparative study
There is a growing interest in applying deep reinforcement learning (DRL) methods to
optimizing the operation of wireless networks. In this paper, we compare three state of the art …
optimizing the operation of wireless networks. In this paper, we compare three state of the art …
Distributed beamforming techniques for cell-free wireless networks using deep reinforcement learning
In a cell-free network, a large number of mobile devices are served simultaneously by
several base stations (BSs)/access points (APs) using the same time/frequency resources …
several base stations (BSs)/access points (APs) using the same time/frequency resources …
Optimization theory based deep reinforcement learning for resource allocation in ultra-reliable wireless networked control systems
The design of Wireless Networked Control System (WNCS) requires addressing critical
interactions between control and communication systems with minimal complexity and …
interactions between control and communication systems with minimal complexity and …
Toward safe and accelerated deep reinforcement learning for next-generation wireless networks
Deep reinforcement learning (DRL) algorithms have recently gained wide attention in the
wireless networks domain. They are considered promising approaches for solving dynamic …
wireless networks domain. They are considered promising approaches for solving dynamic …
Federated deep reinforcement learning for user access control in open radio access networks
The Open Radio Access Network (O-RAN) introducing a particular unit known as RAN
Intelligent Controllers (RICs) has been regarded as revolutionary paradigms to support …
Intelligent Controllers (RICs) has been regarded as revolutionary paradigms to support …
Employing intelligent aerial data aggregators for the internet of things: Challenges and solutions
Internet-of-Things (IoT) devices equipped with temperature and humidity sensors and
cameras are increasingly deployed to monitor remote and human-unfriendly areas (eg …
cameras are increasingly deployed to monitor remote and human-unfriendly areas (eg …
DeepWiERL: Bringing deep reinforcement learning to the internet of self-adaptive things
F Restuccia, T Melodia - IEEE INFOCOM 2020-IEEE …, 2020 - ieeexplore.ieee.org
Recent work has demonstrated that cutting-edge advances in deep reinforcement learning
(DRL) may be leveraged to empower wireless devices with the much-needed ability to" …
(DRL) may be leveraged to empower wireless devices with the much-needed ability to" …
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 …