Fedhql: Federated heterogeneous q-learning
Federated Reinforcement Learning (FedRL) encourages distributed agents to learn
collectively from each other's experience to improve their performance without exchanging …
collectively from each other's experience to improve their performance without exchanging …
Explora: Ai/ml explainability for the open ran
The Open Radio Access Network (RAN) paradigm is transforming cellular networks into a
system of disaggregated, virtualized, and software-based components. These self-optimize …
system of disaggregated, virtualized, and software-based components. These self-optimize …
Joint UAV Deployment and Resource Allocation: a Personalized Federated Deep Reinforcement Learning Approach
Unmanned aerial vehicles (UAVs) are capable of serving as aerial base stations (BSs) for
providing dynamic coverage and connectivity extension for the sixth-generation (6G) …
providing dynamic coverage and connectivity extension for the sixth-generation (6G) …
A Multi-Task Approach to Robust Deep Reinforcement Learning for Resource Allocation
S Gracla, C Bockelmann… - WSA & SCC 2023; 26th …, 2023 - ieeexplore.ieee.org
With increasing complexity of modern communication systems, Machine Learning (ML)
algorithms have become a focal point of research. However, performance demands have …
algorithms have become a focal point of research. However, performance demands have …
Self-optimization of cellular networks using deep reinforcement learning with hybrid action space
Wireless networks have been going through tremendous proliferation recently. As a result, a
continuous configuration and management are necessary to sustain a balanced …
continuous configuration and management are necessary to sustain a balanced …
Adaptive client selection in resource constrained federated learning systems: A deep reinforcement learning approach
H Zhang, Z Xie, R Zarei, T Wu, K Chen - IEEE Access, 2021 - ieeexplore.ieee.org
With data increasingly collected by end devices and the number of devices is growing
rapidly in which data source mainly located outside the cloud today. To guarantee data …
rapidly in which data source mainly located outside the cloud today. To guarantee data …
Learning to schedule communication in multi-agent reinforcement learning
Many real-world reinforcement learning tasks require multiple agents to make sequential
decisions under the agents' interaction, where well-coordinated actions among the agents …
decisions under the agents' interaction, where well-coordinated actions among the agents …
Deep reinforcement learning-based resource allocation in cooperative UAV-assisted wireless networks
We consider the downlink of an unmanned aerial vehicle (UAV) assisted cellular network
consisting of multiple cooperative UAVs, whose operations are coordinated by a central …
consisting of multiple cooperative UAVs, whose operations are coordinated by a central …
Diffusion-based reinforcement learning for edge-enabled AI-generated content services
As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently
generate content is paramount. AI-Generated Content (AIGC) emerges as a key solution, yet …
generate content is paramount. AI-Generated Content (AIGC) emerges as a key solution, yet …
Guest Editorial Special Issue on Distributed Learning Over Wireless Edge Networks—Part II
This is Part II of a double-part special issue on distributed learning over wireless edge
networks. This two-part special issue features papers dealing with two main research …
networks. This two-part special issue features papers dealing with two main research …