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 …
Power control in energy harvesting multiple access system with reinforcement learning
The Internet of Things (IoT) application has a crucial need for long-term and self-sustainable
operations. Energy harvesting (EH) technique has attracted great attention in IoT as it may …
operations. Energy harvesting (EH) technique has attracted great attention in IoT as it may …
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 …
Backhaul-aware user association and resource allocation for energy-constrained HetNets
Growing attention has been paid to renewable-or hybrid-energy-powered heterogeneous
networks (HetNets). In this paper, focusing on backhaul-aware joint user association and …
networks (HetNets). In this paper, focusing on backhaul-aware joint user association and …
TACT: A transfer actor-critic learning framework for energy saving in cellular radio access networks
Recent works have validated the possibility of improving energy efficiency in radio access
networks (RANs), achieved by dynamically turning on/off some base stations (BSs). In this …
networks (RANs), achieved by dynamically turning on/off some base stations (BSs). In this …
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 …
Two-dimensional optimization on user association and green energy allocation for HetNets with hybrid energy sources
In green communications, it is imperative to reduce the total on-grid energy consumption as
well as minimize the peak on-grid energy consumption, since the large peak on-grid energy …
well as minimize the peak on-grid energy consumption, since the large peak on-grid energy …
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 …
Reinforcement learning-based multiaccess control and battery prediction with energy harvesting in IoT systems
Energy harvesting (EH) is a promising technique to fulfill the long-term and self-sustainable
operations for Internet of Things (IoT) systems. In this paper, we study the joint access …
operations for Internet of Things (IoT) systems. In this paper, we study the joint access …
Dynamic channel access and power control in wireless interference networks via multi-agent deep reinforcement learning
Due to the scarcity in the wireless spectrum and limited energy resources especially in
mobile applications, efficient resource allocation strategies are critical in wireless networks …
mobile applications, efficient resource allocation strategies are critical in wireless networks …
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