Concurrent decentralized channel allocation and access point selection using multi-armed bandits in multi BSS WLANs
Á López-Raventós, B Bellalta - Computer Networks, 2020 - Elsevier
Abstract Enterprise Wireless Local Area Networks (WLANs) consist of multiple Access Points
(APs) covering a given area. In these networks, interference is mitigated by allocating …
(APs) covering a given area. In these networks, interference is mitigated by allocating …
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 …
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 …
Convergence time minimization of federated learning over wireless networks
In this paper, the convergence time of federated learning (FL), when deployed over a
realistic wireless network, is studied. In particular, with the considered model, wireless users …
realistic wireless network, is studied. In particular, with the considered model, wireless users …
Smart edge-enabled traffic light control: Improving reward-communication trade-offs with federated reinforcement learning
Traffic congestion is a costly phenomenon of every-day life. Reinforcement Learning (RL) is
a promising solution due to its applicability to solving complex decision-making problems in …
a promising solution due to its applicability to solving complex decision-making problems in …
Optimization for reinforcement learning: From a single agent to cooperative agents
Fueled by recent advances in deep neural networks, reinforcement learning (RL) has been
in the limelight because of many recent breakthroughs in artificial intelligence, including …
in the limelight because of many recent breakthroughs in artificial intelligence, including …
Deep reinforcement learning for Internet of Things: A comprehensive survey
The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in
communication, computing, caching and control (4Cs) problems. The recent advances in …
communication, computing, caching and control (4Cs) problems. The recent advances in …
An overview of intelligent wireless communications using deep reinforcement learning
Future wireless communication networks tend to be intelligentized to accomplish the
missions that cannot be preprogrammed. In the new intelligent communication systems …
missions that cannot be preprogrammed. In the new intelligent communication systems …
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 …
Learning radio resource management in RANs: Framework, opportunities, and challenges
In the fifth generation (5G) of mobile broadband systems, radio resource management
(RRM) will reach unprecedented levels of complexity. To cope with the ever more …
(RRM) will reach unprecedented levels of complexity. To cope with the ever more …