Applications of deep reinforcement learning in communications and networking: A survey
This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
A survey on machine learning in Internet of Things: Algorithms, strategies, and applications
In the IoT and WSN era, large number of connected objects and sensing devices are
dedicated to collect, transfer, and generate a huge amount of data for a wide variety of fields …
dedicated to collect, transfer, and generate a huge amount of data for a wide variety of fields …
A gentle introduction to reinforcement learning and its application in different fields
Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …
become one of the most important and useful technology. It is a learning method where a …
Deep multi-user reinforcement learning for distributed dynamic spectrum access
O Naparstek, K Cohen - IEEE transactions on wireless …, 2018 - ieeexplore.ieee.org
We consider the problem of dynamic spectrum access for network utility maximization in
multichannel wireless networks. The shared bandwidth is divided into K orthogonal …
multichannel wireless networks. The shared bandwidth is divided into K orthogonal …
Deep reinforcement learning for dynamic multichannel access in wireless networks
We consider a dynamic multichannel access problem, where multiple correlated channels
follow an unknown joint Markov model and users select the channel to transmit data. The …
follow an unknown joint Markov model and users select the channel to transmit data. The …
Deep-reinforcement learning multiple access for heterogeneous wireless networks
This paper investigates a deep reinforcement learning (DRL)-based MAC protocol for
heterogeneous wireless networking, referred to as a Deep-reinforcement Learning Multiple …
heterogeneous wireless networking, referred to as a Deep-reinforcement Learning Multiple …
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 user association and resource allocation in heterogeneous networks
Heterogeneous networks (HetNets) can offload the traffic and reduce the deployment cost,
which is regarded as a promising technique in next-generation cellular networks. Because …
which is regarded as a promising technique in next-generation cellular networks. Because …
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 …
Joint traffic control and multi-channel reassignment for core backbone network in SDN-IoT: a multi-agent deep reinforcement learning approach
Channel reassignment is to assign again on the assigned channel resources in order to use
the channel resources more efficiently. Channel reassignment in the Software-Defined …
the channel resources more efficiently. Channel reassignment in the Software-Defined …