Reinforcement learning for context awareness and intelligence in wireless networks: Review, new features and open issues
In wireless networks, context awareness and intelligence are capabilities that enable each
host to observe, learn, and respond to its complex and dynamic operating environment in an …
host to observe, learn, and respond to its complex and dynamic operating environment in an …
Deep reinforcement learning for internet of drones networks: issues and research directions
N Aboueleneen, A Alwarafy… - IEEE Open Journal of the …, 2023 - ieeexplore.ieee.org
Internet of Drones (IoD) is one of the promising technologies to enhance the performance of
wireless networks. Deploying IoD to assist wireless networks, however, needs to address …
wireless networks. Deploying IoD to assist wireless networks, however, needs to address …
Semantic-aware collaborative deep reinforcement learning over wireless cellular networks
Collaborative deep reinforcement learning (CDRL) algorithms in which multiple agents can
coordinate over a wireless network is a promising approach to enable future intelligent and …
coordinate over a wireless network is a promising approach to enable future intelligent and …
Cooperative multi-agent reinforcement learning for low-level wireless communication
Traditional radio systems are strictly co-designed on the lower levels of the OSI stack for
compatibility and efficiency. Although this has enabled the success of radio communications …
compatibility and efficiency. Although this has enabled the success of radio communications …
Optimization theory based deep reinforcement learning for resource allocation in ultra-reliable wireless networked control systems
The design of Wireless Networked Control System (WNCS) requires addressing critical
interactions between control and communication systems with minimal complexity and …
interactions between control and communication systems with minimal complexity and …
Deep reinforcement learning: A survey
X Wang, S Wang, X Liang, D Zhao… - … on Neural Networks …, 2022 - ieeexplore.ieee.org
Deep reinforcement learning (DRL) integrates the feature representation ability of deep
learning with the decision-making ability of reinforcement learning so that it can achieve …
learning with the decision-making ability of reinforcement learning so that it can achieve …
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 …
Deep reinforcement learning
M Krichen - 2023 14th International Conference on Computing …, 2023 - ieeexplore.ieee.org
Deep Reinforcement Learning (DRL) is a powerful technique for learning policies for
complex decision-making tasks. In this paper, we provide an overview of DRL, including its …
complex decision-making tasks. In this paper, we provide an overview of DRL, including its …
[HTML][HTML] A survey on applications of reinforcement learning in flying ad-hoc networks
Flying ad-hoc networks (FANET) are one of the most important branches of wireless ad-hoc
networks, consisting of multiple unmanned air vehicles (UAVs) performing assigned tasks …
networks, consisting of multiple unmanned air vehicles (UAVs) performing assigned tasks …
Thirty years of machine learning: The road to Pareto-optimal wireless networks
Future wireless networks have a substantial potential in terms of supporting a broad range of
complex compelling applications both in military and civilian fields, where the users are able …
complex compelling applications both in military and civilian fields, where the users are able …