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 …
Safe and Accelerated Deep Reinforcement Learning-based O-RAN Slicing: A Hybrid Transfer Learning Approach
AM Nagib, H Abou-Zeid… - IEEE Journal on Selected …, 2023 - ieeexplore.ieee.org
The open radio access network (O-RAN) architecture supports intelligent network control
algorithms as one of its core capabilities. Data-driven applications incorporate such …
algorithms as one of its core capabilities. Data-driven applications incorporate such …
The frontiers of deep reinforcement learning for resource management in future wireless HetNets: Techniques, challenges, and research directions
Next generation wireless networks are expected to be extremely complex due to their
massive heterogeneity in terms of the types of network architectures they incorporate, the …
massive heterogeneity in terms of the types of network architectures they incorporate, the …
Deep reinforcement learning for radio resource allocation and management in next generation heterogeneous wireless networks: A survey
Next generation wireless networks are expected to be extremely complex due to their
massive heterogeneity in terms of the types of network architectures they incorporate, the …
massive heterogeneity in terms of the types of network architectures they incorporate, the …
Reinforcement learning meets wireless networks: A layering perspective
Driven by the soaring traffic demand and the growing diversity of mobile services, wireless
networks are evolving to be increasingly dense and heterogeneous. Accordingly, in such …
networks are evolving to be increasingly dense and heterogeneous. Accordingly, in such …
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 …
Introduction to the special section on deep reinforcement learning for future wireless communication networks
We are delighted to introduce the readers to this special section of the IEEE Transactions on
Cognitive Communications and Networking (TCCN), which aims at exploring recent …
Cognitive Communications and Networking (TCCN), which aims at exploring recent …
Green deep reinforcement learning for radio resource management: Architecture, algorithm compression, and challenges
Artificial intelligence (AI) heralds a step-change in wireless networks but may also cause
irreversible environmental damage due to its high energy consumption. Here, we address …
irreversible environmental damage due to its high energy consumption. Here, we address …
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 …
Machine learning-enabled radio resource management for next-generation wireless networks
M Elsayed - 2021 - ruor.uottawa.ca
A new era of wireless networks is evolving, thanks to the significant advances in
communications and networking technologies. In parallel, wireless services are witnessing a …
communications and networking technologies. In parallel, wireless services are witnessing a …
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