Wireless power control via counterfactual optimization of graph neural networks
N Naderializadeh, M Eisen… - 2020 IEEE 21st …, 2020 - ieeexplore.ieee.org
We consider the problem of downlink power control in wireless networks, consisting of
multiple transmitter-receiver pairs communicating with each other over a single shared …
multiple transmitter-receiver pairs communicating with each other over a single shared …
Large scale wireless power allocation with graph neural networks
We consider the optimal power allocation with a set of transmitter/receiver pairs in a large
scale wireless network. Obtaining an optimal policy requires the solving of a non-convex …
scale wireless network. Obtaining an optimal policy requires the solving of a non-convex …
Adaptive wireless power allocation with graph neural networks
N NaderiAlizadeh, M Eisen… - ICASSP 2022-2022 IEEE …, 2022 - ieeexplore.ieee.org
We consider the problem of power control in wireless networks, consisting of multiple
transmitter-receiver pairs communicating with each other over a single shared wireless …
transmitter-receiver pairs communicating with each other over a single shared wireless …
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 …
Fast power control adaptation via meta-learning for random edge graph neural networks
I Nikoloska, O Simeone - 2021 IEEE 22nd International …, 2021 - ieeexplore.ieee.org
Power control in decentralized wireless networks poses a complex stochastic optimization
problem when formulated as the maximization of the average sum rate for arbitrary …
problem when formulated as the maximization of the average sum rate for arbitrary …
Deep learning for robust power control for wireless networks
Robust optimization is an important task in wireless communications, because due to fading
and feedback delay there is inherent uncertainty in channel state information in a wireless …
and feedback delay there is inherent uncertainty in channel state information in a wireless …
Modular meta-learning for power control via random edge graph neural networks
I Nikoloska, O Simeone - IEEE Transactions on Wireless …, 2022 - ieeexplore.ieee.org
In this paper, we consider the problem of power control for a wireless network with an
arbitrarily time-varying topology, including the possible addition or removal of nodes. A data …
arbitrarily time-varying topology, including the possible addition or removal of nodes. A data …
Unsupervised-learning power control for cell-free wireless systems
This paper studies the viability of feedforward neural networks (NNs) for centralized power
control in the uplink of cell-free wireless systems with matched-filter reception. The …
control in the uplink of cell-free wireless systems with matched-filter reception. The …
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 multi-agent power control in wireless networks with frequency reuse
Many of the existing optimization-based transmit power control algorithms suffer from high
computational complexity and require instantaneous global channel state information (CSI) …
computational complexity and require instantaneous global channel state information (CSI) …