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 …
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 …
Power control for interference management 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 …
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 …
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 …
Deep learning based resource allocation: How much training data is needed?
We consider artificial neural networks based energy-efficient power control for interference
networks. The influence of different training set sizes and data augmentation is evaluated. It …
networks. The influence of different training set sizes and data augmentation is evaluated. It …
Deep reinforcement learning-assisted optimization for resource allocation in downlink OFDMA cooperative systems
MK Tefera, S Zhang, Z Jin - Entropy, 2023 - mdpi.com
This paper considers a downlink resource-allocation problem in distributed interference
orthogonal frequency-division multiple access (OFDMA) systems under maximal power …
orthogonal frequency-division multiple access (OFDMA) systems under maximal power …
[PDF][PDF] A Deep Reinforcement Learning-Based Technique for Optimal Power Allocation in Multiple Access Communications.
For many years, researchers have explored power allocation (PA) algorithms driven by
models in wireless networks where multiple-user communications with interference are …
models in wireless networks where multiple-user communications with interference are …
Deep reinforcement learning based dynamic power and beamforming design for time-varying wireless downlink interference channel
In the wireless communication, deep reinforcement learning (DRL) techniques promise
performance optimizations at a low cost. Considering the time-varying property of the …
performance optimizations at a low cost. Considering the time-varying property of the …
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 …