QoS-aware power management with deep learning
Network densification is becoming an overwhelming phenomenon in many emerging
wireless communication paradigms. Although network densification may promote system …
wireless communication paradigms. Although network densification may promote system …
Power control with QoS guarantees: A differentiable projection-based unsupervised learning framework
M Alizadeh, H Tabassum - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Deep neural networks (DNNs) are emerging as a potential solution to solve NP-hard
wireless resource allocation problems. However, in the presence of intricate constraints, eg …
wireless resource allocation problems. However, in the presence of intricate constraints, eg …
WIP: Demand-driven power allocation in wireless networks with deep Q-learning
A Giannopoulos, S Spantideas… - 2021 IEEE 22nd …, 2021 - ieeexplore.ieee.org
Power allocation is strongly related to the coverage and capacity of wireless networks,
playing a critical role in the development of 5G networks. This paper proposes a Demand …
playing a critical role in the development of 5G networks. This paper proposes a Demand …
Deep convolutional neural network assisted reinforcement learning based mobile network power saving
S Wu, Y Wang, L Bai - IEEE Access, 2020 - ieeexplore.ieee.org
This paper addresses the power saving problem in mobile networks. Base station (BS)
power and network traffic volume (NTV) models are first established. The BS power is …
power and network traffic volume (NTV) models are first established. The BS power is …
Convolutional neural network based optimization approach for wireless resource management
In this paper, the feasibility of evolving advanced deep learning technology is demonstrated
to solve the NP-hard transmit power control problem for future wireless networks. In the …
to solve the NP-hard transmit power control problem for future wireless networks. In the …
Towards optimal power control via ensembling deep neural networks
A deep neural network (DNN) based power control method that aims at solving the non-
convex optimization problem of maximizing the sum rate of a fading multi-user interference …
convex optimization problem of maximizing the sum rate of a fading multi-user interference …
Qos-aware power management in LTE-a networks under heterogeneous traffics
ME Özçevik, G Seçinti… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
In 3GPP LTE-A releases v. 8 and beyond, the plug and play nature of the eNodeBs lets a
dense deployment of eNodeBs, thus, causing over-provisioned network infrastructure. Here …
dense deployment of eNodeBs, thus, causing over-provisioned network infrastructure. Here …
Federated power control for predictive qos in 5g and beyond: A proof of concept for urllc
The fifth-generation (5G) mobile standard has been designed to support new use cases
such as ultra-reliable and low-latency communication (URLLC). The future 6G is envisioned …
such as ultra-reliable and low-latency communication (URLLC). The future 6G is envisioned …
A deep neural network based optimization approach for wireless resource management
This paper demonstrates the feasibility of emerging disruptive deep learning technology to
solve NP-hard transmit power control problem in future wireless networks. Existing …
solve NP-hard transmit power control problem in future wireless networks. Existing …
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 …