Distributed deep reinforcement learning-based spectrum and power allocation for heterogeneous networks
This paper investigates the problem of distributed resource management in two-tier
heterogeneous networks, where each cell selects its joint device association, spectrum …
heterogeneous networks, where each cell selects its joint device association, spectrum …
Energy-efficient power allocation and user association in heterogeneous networks with deep reinforcement learning
CK Hsieh, KL Chan, FT Chien - Applied Sciences, 2021 - mdpi.com
This paper studies the problem of joint power allocation and user association in wireless
heterogeneous networks (HetNets) with a deep reinforcement learning (DRL)-based …
heterogeneous networks (HetNets) with a deep reinforcement learning (DRL)-based …
Deep reinforcement learning for user association and resource allocation in heterogeneous cellular networks
Heterogeneous cellular networks can offload the mobile traffic and reduce the deployment
costs, which have been considered to be a promising technique in the next-generation …
costs, which have been considered to be a promising technique in the next-generation …
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 …
Multi-agent reinforcement learning-based distributed dynamic spectrum access
Dynamic spectrum access (DSA) is an effective solution for efficiently utilizing the radio
spectrum by sharing it among various networks. Two primary tasks of a DSA controller are …
spectrum by sharing it among various networks. Two primary tasks of a DSA controller are …
Power control based on deep reinforcement learning for spectrum sharing
In the current researches, artificial intelligence (AI) plays a crucial role in resource
management for the next generation wireless communication network. However, traditional …
management for the next generation wireless communication network. However, traditional …
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 …
Deep reinforcement learning for multi-agent power control in heterogeneous networks
We consider a typical heterogeneous network (HetNet), in which multiple access points
(APs) are deployed to serve users by reusing the same spectrum band. Since different APs …
(APs) are deployed to serve users by reusing the same spectrum band. Since different APs …
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-based spectrum allocation in integrated access and backhaul networks
We develop a framework based on deep reinforcement learning (DRL) to solve the spectrum
allocation problem in the emerging integrated access and backhaul (IAB) architecture with …
allocation problem in the emerging integrated access and backhaul (IAB) architecture with …
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