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 …
Power allocation in multi-user cellular networks: Deep reinforcement learning approaches
The model-based power allocation has been investigated for decades, but this approach
requires mathematical models to be analytically tractable and it has high computational …
requires mathematical models to be analytically tractable and it has high computational …
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 …
Learning to branch: Accelerating resource allocation in wireless networks
Resource allocation in wireless networks, such as device-to-device (D2D) communications,
is usually formulated as mixed integer nonlinear programming (MINLP) problems, which are …
is usually formulated as mixed integer nonlinear programming (MINLP) problems, which are …
A reinforcement learning approach to power control and rate adaptation in cellular networks
Optimizing radio transmission power and user data rates in wireless systems requires full
system observability. While the problem has been extensively studied in the literature …
system observability. While the problem has been extensively studied in the literature …
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 …
Power allocation in multi-user cellular networks with deep Q learning approach
The model-driven power allocation (PA) algorithms in the wireless cellular networks with
interfering multiple-access channel (IMAC) have been investigated for decades. Nowadays …
interfering multiple-access channel (IMAC) have been investigated for decades. Nowadays …
A graph neural network approach for scalable wireless power control
Deep neural networks have recently emerged as a disruptive technology to solve NP-hard
wireless resource allocation problems in a real-time manner. However, the adopted neural …
wireless resource allocation problems in a real-time manner. However, the adopted neural …
Resource management in wireless networks via multi-agent deep reinforcement learning
We propose a mechanism for distributed resource management and interference mitigation
in wireless networks using multi-agent deep reinforcement learning (RL). We equip each …
in wireless networks using multi-agent deep reinforcement learning (RL). We equip each …
Joint physical-layer and system-level power management for delay-sensitive wireless communications
N Mastronarde… - IEEE transactions on …, 2012 - ieeexplore.ieee.org
We consider the problem of energy-efficient point-to-point transmission of delay-sensitive
data (eg, multimedia data) over a fading channel. Existing research on this topic utilizes …
data (eg, multimedia data) over a fading channel. Existing research on this topic utilizes …
相关搜索
- dynamic power allocation
- resource allocation in wireless networks
- power control in wireless networks
- deep reinforcement power control
- power allocation learning approaches
- wireless networks joint scheduling
- channel access power control
- multi-user cellular networks
- rate adaptation power control
- wireless interference networks
- wireless communications power management
- deep reinforcement heterogeneous networks
- resource management in wireless networks
- heterogeneous networks power control
- cellular networks power control
- joint scheduling power control