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 …
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 …
AI-based radio resource allocation in support of the massive heterogeneity of 6G networks
There is a consensus in industry and academia that 6G wireless networks will incorporate
massive heterogeneous radio access technologies (RATs) in order to cater to the high …
massive heterogeneous radio access technologies (RATs) in order to cater to the high …
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 …
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 …
DeepRAT: A DRL-based framework for multi-RAT assignment and power allocation in HetNets
Wireless heterogeneous networks (HetNets), where several systems with multi-radio access
technologies (multi-RATs) coexist for massive multi-connectivity networks, are in service …
technologies (multi-RATs) coexist for massive multi-connectivity networks, are in service …
Radio resource scheduling with deep pointer networks and reinforcement learning
This article presents an artificial intelligence (AI) adaptable solution to handle the radio
resource scheduling (RRS) task in 5G networks. RRS is one of the core tasks in radio …
resource scheduling (RRS) task in 5G networks. RRS is one of the core tasks in radio …
Transfer learning-based accelerated deep reinforcement learning for 5G RAN slicing
AM Nagib, H Abou-Zeid… - 2021 IEEE 46th …, 2021 - ieeexplore.ieee.org
Deep Reinforcement Learning (DRL) algorithms have been recently proposed to solve
dynamic Radio Resource Management (RRM) problems in 5G networks. However, the slow …
dynamic Radio Resource Management (RRM) problems in 5G networks. However, the slow …
A survey on applications of deep reinforcement learning in resource management for 5G heterogeneous networks
YL Lee, D Qin - 2019 Asia-Pacific Signal and Information …, 2019 - ieeexplore.ieee.org
Heterogeneous networks (HetNets) have been regarded as the key technology for fifth
generation (5G) communications to support the explosive growth of mobile traffics. By …
generation (5G) communications to support the explosive growth of mobile traffics. By …
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 …
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