Optimal radio resource management in 5G NR featuring network slicing
Abstract 3GPP 5G New Radio (NR) has introduced several new features that the network
slicing concept can leverage to guarantee the heterogeneous requirements in terms of …
slicing concept can leverage to guarantee the heterogeneous requirements in terms of …
Team learning-based resource allocation for open radio access network (O-RAN)
Recently, the concept of open radio access network (O-RAN) has been proposed, which
aims to adopt intelligence and openness in the next generation radio access networks …
aims to adopt intelligence and openness in the next generation radio access networks …
Radio and energy resource management in renewable energy-powered wireless networks with deep reinforcement learning
In this paper, we study radio and energy resource management in renewable energy-
powered wireless networks, where base stations (BSs) are powered by both on-grid and …
powered wireless networks, where base stations (BSs) are powered by both on-grid and …
Resource allocation for IRS-assisted networks: A deep reinforcement learning approach
In a wireless communication network, the propagation medium has been perceived as a
randomly behaving entity between the transmitter and receiver for a long time. However …
randomly behaving entity between the transmitter and receiver for a long time. However …
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 …
Policy-gradient-based reinforcement learning for computing resources allocation in o-ran
Open Radio Access Network (O-RAN) is a novel architecture aiming to disaggregate the
network components to reduce capital and operational costs and open the interfaces to …
network components to reduce capital and operational costs and open the interfaces to …
Reinforcement and deep reinforcement learning for wireless Internet of Things: A survey
Nowadays, many research studies and industrial investigations have allowed the integration
of the Internet of Things (IoT) in current and future networking applications by deploying a …
of the Internet of Things (IoT) in current and future networking applications by deploying a …
Deep reinforcement learning radio control and signal detection with kerlym, a gym rl agent
This paper presents research in progress investigating the viability and adaptation of
reinforcement learning using deep neural network based function approximation for the task …
reinforcement learning using deep neural network based function approximation for the task …
A novel duplex deep reinforcement learning based RRM framework for next-generation V2X communication networks
Resource management in the next-generation vehicle-to-everything (V2X) communication
networks is a demanding research problem. It is difficult to achieve the best results if the …
networks is a demanding research problem. It is difficult to achieve the best results if the …
Deep reinforcement learning for user association and resource allocation in heterogeneous cellular networks
Heterogeneous cellular networks can offload the mobile traffic and reduce the deployment
costs, which have been considered to be a promising technique in the next-generation …
costs, which have been considered to be a promising technique in the next-generation …