Meta federated reinforcement learning for distributed resource allocation
In cellular networks, resource allocation is usually performed in a centralized way, which
brings huge computation complexity to the base station (BS) and high transmission …
brings huge computation complexity to the base station (BS) and high transmission …
Federated learning for distributed energy-efficient resource allocation
In cellular networks, resource allocation is performed in a centralized way, which brings
huge computation complexity to the base station (BS) and high transmission overhead. This …
huge computation complexity to the base station (BS) and high transmission overhead. This …
Federated learning based resource allocation for wireless communication networks
P Behmandpoor, P Patrinos… - 2022 30th European …, 2022 - ieeexplore.ieee.org
In this paper we introduce federated learning (FL) based resource allocation (RA) for
wireless communication networks, where users cooperatively train a RA policy in a …
wireless communication networks, where users cooperatively train a RA policy in a …
A Collaborative Multi-agent Deep Reinforcement Learning-based Wireless Power Allocation with Centralized Training and Decentralized Execution
Despite the success of Deep Reinforcement Learning (DRL) in radio-resource management
within multi-cell wireless networks, applying it to power allocation in ultra-dense 5G and …
within multi-cell wireless networks, applying it to power allocation in ultra-dense 5G and …
Joint Device Participation, Dataset Management, and Resource Allocation in Wireless Federated Learning via Deep Reinforcement Learning
Federated Learning (FL) enables large-scale machine learning without uploading the
private data of wireless devices. Due to the heterogeneity and limitation of the devices' …
private data of wireless devices. Due to the heterogeneity and limitation of the devices' …
Joint ddpg and unsupervised learning for channel allocation and power control in centralized wireless cellular networks
M Sun, E Mei, S Wang, Y Jin - Ieee Access, 2023 - ieeexplore.ieee.org
In order to solve the resource allocation problem in scenarios of centralized wireless cellular
communication with multiple cells, users and channels, a novel resource allocation …
communication with multiple cells, users and channels, a novel resource allocation …
Learning-based multi-objective resource allocation for over-the-air federated learning
Over-the-air federated learning (AirFL) has developed as a communication-efficient solution
to enable distributed machine learning over edge devices by integrating computation and …
to enable distributed machine learning over edge devices by integrating computation and …
Federated cooperation and augmentation for power allocation in decentralized wireless networks
Emerging mobile edge techniques and applications such as Augmented Reality (AR)/Virtual
Reality (VR), Internet of Things (IoT), and vehicle networking, result in an explosive growth of …
Reality (VR), Internet of Things (IoT), and vehicle networking, result in an explosive growth of …
Distributed deep reinforcement learning-based spectrum and power allocation for heterogeneous networks
This paper investigates the problem of distributed resource management in two-tier
heterogeneous networks, where each cell selects its joint device association, spectrum …
heterogeneous networks, where each cell selects its joint device association, spectrum …
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 …