Efficient training management for mobile crowd-machine learning: A deep reinforcement learning approach
In this letter, we consider the concept of mobile crowd-machine learning (MCML) for a
federated learning model. The MCML enables mobile devices in a mobile network to …
federated learning model. The MCML enables mobile devices in a mobile network to …
Applying deep reinforcement learning to improve throughput and reduce collision rate in IEEE 802.11 networks
Abstract The effectiveness of Wi-Fi networks is greatly influenced by the optimization of
contention window (CW) parameters. Unfortunately, the conventional approach employed …
contention window (CW) parameters. Unfortunately, the conventional approach employed …
Deep reinforcement learning-based mobility-aware robust proactive resource allocation in heterogeneous networks
J Li, X Zhang, J Zhang, J Wu, Q Sun… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Proactive resource allocation (PRA) is an essential technology boosting intelligent
communication, as it can make full use of prediction and significantly improve network …
communication, as it can make full use of prediction and significantly improve network …
Age of information aware radio resource management in vehicular networks: A proactive deep reinforcement learning perspective
In this paper, we investigate the problem of age of information (AoI)-aware radio resource
management for expected long-term performance optimization in a Manhattan grid vehicle …
management for expected long-term performance optimization in a Manhattan grid vehicle …
A study on deep learning for latency constraint applications in beyond 5G wireless systems
The fifth generation (5G) of wireless communications has led to many advancements in
technologies such as large and distributed antenna arrays, ultra-dense networks, software …
technologies such as large and distributed antenna arrays, ultra-dense networks, software …
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" …
O-RAN AI/ML workflow implementation of personalized network optimization via reinforcement learning
H Lee, Y Jang, J Song, H Yeon - 2021 IEEE Globecom …, 2021 - ieeexplore.ieee.org
In this paper, we study AI-based RAN technology for 5G and 6G networks that are more
complex and difficult to analyze than previous generations to make the network more …
complex and difficult to analyze than previous generations to make the network more …
Intelligent user association for symbiotic radio networks using deep reinforcement learning
In this paper, we are interested in symbiotic radio networks (SRNs), in which an Internet-of-
Things (IoT) network parasitizes in a primary cellular network to achieve spectrum-, energy …
Things (IoT) network parasitizes in a primary cellular network to achieve spectrum-, energy …
Incorporating distributed DRL into storage resource optimization of space-air-ground integrated wireless communication network
Space-air-ground integrated network (SAGIN) is a new type of wireless network mode. The
effective management of SAGIN resources is a prerequisite for high-reliability …
effective management of SAGIN resources is a prerequisite for high-reliability …
Learning to continuously optimize wireless resource in episodically dynamic environment
There has been a growing interest in developing data-driven, in particular deep neural
network (DNN) based methods for modern communication tasks. For a few popular tasks …
network (DNN) based methods for modern communication tasks. For a few popular tasks …