Deep-learning-based wireless resource allocation with application to vehicular networks
It has been a long-held belief that judicious resource allocation is critical to mitigating
interference, improving network efficiency, and ultimately optimizing wireless communication …
interference, improving network efficiency, and ultimately optimizing wireless communication …
When multiple agents learn to schedule: A distributed radio resource management framework
Interference among concurrent transmissions in a wireless network is a key factor limiting
the system performance. One way to alleviate this problem is to manage the radio resources …
the system performance. One way to alleviate this problem is to manage the radio resources …
Introduction to the special section on deep reinforcement learning for future wireless communication networks
We are delighted to introduce the readers to this special section of the IEEE Transactions on
Cognitive Communications and Networking (TCCN), which aims at exploring recent …
Cognitive Communications and Networking (TCCN), which aims at exploring recent …
Joint device scheduling and bandwidth allocation for federated learning over wireless networks
Federated Learning (FL) has been widely used to train shared machine learning models
while addressing the privacy concerns. When deployed in wireless networks, bandwidth …
while addressing the privacy concerns. When deployed in wireless networks, bandwidth …
Deep neural networks with data rate model: Learning power allocation efficiently
J Guo, C Yang - IEEE Transactions on Communications, 2023 - ieeexplore.ieee.org
Learning-based resource allocation can be implemented in real-time, but deep neural
networks (DNNs) developed in other fields such as computer vision are with high training …
networks (DNNs) developed in other fields such as computer vision are with high training …
Federated-based deep reinforcement learning (Fed-DRL) for energy management in a distributive wireless network
Studies on developing future generation wireless systems are expected to support
increased infrastructure development and device subscriptions with densely deployed base …
increased infrastructure development and device subscriptions with densely deployed base …
Deep reinforcement learning for wireless network
The rapid introduction of mobile devices and the growing popularity of mobile applications
and services create unprecedented infrastructure requirements for mobile and wireless …
and services create unprecedented infrastructure requirements for mobile and wireless …
Multi-agent deep reinforcement learning multiple access for heterogeneous wireless networks with imperfect channels
This paper investigates a futuristic spectrum sharing paradigm for heterogeneous wireless
networks with imperfect channels. In the heterogeneous networks, multiple wireless …
networks with imperfect channels. In the heterogeneous networks, multiple wireless …
FedKL: Tackling data heterogeneity in federated reinforcement learning by penalizing KL divergence
One of the fundamental issues for Federated Learning (FL) is data heterogeneity, which
causes accuracy degradation, slow convergence, and the communication bottleneck issue …
causes accuracy degradation, slow convergence, and the communication bottleneck issue …
Deep reinforcement learning based wireless network optimization: A comparative study
There is a growing interest in applying deep reinforcement learning (DRL) methods to
optimizing the operation of wireless networks. In this paper, we compare three state of the art …
optimizing the operation of wireless networks. In this paper, we compare three state of the art …