DRAG: Deep reinforcement learning based base station activation in heterogeneous networks
J Ye, YJA Zhang - IEEE Transactions on Mobile Computing, 2019 - ieeexplore.ieee.org
Heterogeneous Network (HetNet), where Small cell Base Stations (SBSs) are densely
deployed to offload traffic from macro Base Stations (BSs), is identified as a key solution to …
deployed to offload traffic from macro Base Stations (BSs), is identified as a key solution to …
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 …
Ultra-dense hetnets meet big data: Green frameworks, techniques, and approaches
Ultra-dense heterogeneous networks (Ud-HetNets) have been put forward to improve the
network capacity for next-generation wireless networks. However, counter to the 5G vision …
network capacity for next-generation wireless networks. However, counter to the 5G vision …
Deep reinforcement learning-based mobility-aware robust proactive resource allocation in heterogeneous networks
J Li, X Zhang, J Zhang, J Wu, Q Sun… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Proactive resource allocation (PRA) is an essential technology boosting intelligent
communication, as it can make full use of prediction and significantly improve network …
communication, as it can make full use of prediction and significantly improve network …
Distributed beamforming techniques for cell-free wireless networks using deep reinforcement learning
In a cell-free network, a large number of mobile devices are served simultaneously by
several base stations (BSs)/access points (APs) using the same time/frequency resources …
several base stations (BSs)/access points (APs) using the same time/frequency resources …
Energy-efficient mode selection and resource allocation for D2D-enabled heterogeneous networks: A deep reinforcement learning approach
T Zhang, K Zhu, J Wang - IEEE Transactions on Wireless …, 2020 - ieeexplore.ieee.org
Improving energy efficiency has shown increasing importance in designing future cellular
system. In this work, we consider the issue of energy efficiency in D2D-enabled …
system. In this work, we consider the issue of energy efficiency in D2D-enabled …
Optimal resource allocation considering non-uniform spatial traffic distribution in ultra-dense networks: A multi-agent reinforcement learning approach
Recently, the demand for small cell base stations (SBSs) has been exploding to
accommodate the explosive increase in mobile data traffic. In ultra-dense small cell …
accommodate the explosive increase in mobile data traffic. In ultra-dense small cell …
User scheduling and resource allocation in HetNets with hybrid energy supply: An actor-critic reinforcement learning approach
Densely deployment of various small-cell base stations in cellular networks to increase
capacity will lead to heterogeneous networks (HetNets), and meanwhile, embedding the …
capacity will lead to heterogeneous networks (HetNets), and meanwhile, embedding the …
Performance optimization in mobile-edge computing via deep reinforcement learning
To improve the quality of computation experience for mobile devices, mobile-edge
computing (MEC) is emerging as a promising paradigm by providing computing capabilities …
computing (MEC) is emerging as a promising paradigm by providing computing capabilities …
Energy-efficient ultra-dense network with deep reinforcement learning
With the explosive growth in mobile data traffic, ultra-dense network (UDN) where a large
number of small cells are densely deployed on top of macro cells has received a great deal …
number of small cells are densely deployed on top of macro cells has received a great deal …
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