Joint optimization of handover control and power allocation based on multi-agent deep reinforcement learning
D Guo, L Tang, X Zhang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
In this paper, we study the handover (HO), and power allocation problem in a two-tier
heterogeneous network (HetNet), which consists of a macro base station, and some …
heterogeneous network (HetNet), which consists of a macro base station, and some …
Handover control in wireless systems via asynchronous multiuser deep reinforcement learning
In this paper, we propose a two-layer framework to learn the optimal handover (HO)
controllers in possibly large-scale wireless systems supporting mobile Internet-of-Things …
controllers in possibly large-scale wireless systems supporting mobile Internet-of-Things …
A deep reinforcement learning for user association and power control in heterogeneous networks
Heterogeneous network (HetNet) is a promising solution to satisfy the unprecedented
demand for higher data rate in the next generation mobile networks. Different from the …
demand for higher data rate in the next generation mobile networks. Different from the …
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 …
Asynchronous deep reinforcement learning for data-driven task offloading in MEC-empowered vehicular networks
Mobile edge computing (MEC) has been an effective paradigm to support real-time
computation-intensive vehicular applications. However, due to highly dynamic vehicular …
computation-intensive vehicular applications. However, due to highly dynamic vehicular …
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 …
Multi-agent deep reinforcement learning for dynamic power allocation in wireless networks
This work demonstrates the potential of deep reinforcement learning techniques for transmit
power control in wireless networks. Existing techniques typically find near-optimal power …
power control in wireless networks. Existing techniques typically find near-optimal power …
Decentralized power allocation for MIMO-NOMA vehicular edge computing based on deep reinforcement learning
Vehicular edge computing (VEC) is envisioned as a promising approach to process the
explosive computation tasks of vehicular user (VU). In the VEC system, each VU allocates …
explosive computation tasks of vehicular user (VU). In the VEC system, each VU allocates …
A cooperative charging control strategy for electric vehicles based on multiagent deep reinforcement learning
The growth of electric vehicles (EVs) significantly increases the residential electricity
demand and potentially leads to the overload of the transformer in the distribution grid …
demand and potentially leads to the overload of the transformer in the distribution grid …
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 …
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