FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation
Virtual network embedding (VNE) is an essential resource allocation task in network
virtualization, aiming to map virtual network requests (VNRs) onto physical infrastructure …
virtualization, aiming to map virtual network requests (VNRs) onto physical infrastructure …
Bayesian reinforcement learning for link-level throughput maximization
H Khoshkbari, V Pourahmadi… - IEEE Communications …, 2020 - ieeexplore.ieee.org
One intrinsic property of neural networks is making confident decisions because they do not
capture uncertainty in training data. As a result, when Neural Networks (NN) are used in …
capture uncertainty in training data. As a result, when Neural Networks (NN) are used in …
Learning to Adapt: Communication Load Balancing via Adaptive Deep Reinforcement Learning
The association of mobile devices with network resources (eg, base stations, frequency
bands/channels), known as load balancing, is critical to reduce communication traffic …
bands/channels), known as load balancing, is critical to reduce communication traffic …
Adaptive wireless network management with multi-agent reinforcement learning
Wireless networks are trending towards large scale systems, containing thousands of nodes,
with multiple co-existing applications. Congestion is an inevitable consequence of this scale …
with multiple co-existing applications. Congestion is an inevitable consequence of this scale …
Joint client selection and bandwidth allocation of wireless federated learning by deep reinforcement learning
W Mao, X Lu, Y Jiang, H Zheng - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Federated Learning (FL) is a promising paradigm for massive data mining service while
protecting users' privacy. In wireless federated learning networks (WFLNs), limited …
protecting users' privacy. In wireless federated learning networks (WFLNs), limited …
Reinforcement learning on computational resource allocation of cloud-based wireless networks
B Chen, Y Zhang, G Iosifidis… - 2020 IEEE 6th World …, 2020 - ieeexplore.ieee.org
Wireless networks used for Internet of Things (IoT) are expected to largely involve cloud-
based computing and processing. Softwarised and centralised signal processing and …
based computing and processing. Softwarised and centralised signal processing and …
Uplink scheduling in federated learning: An importance-aware approach via graph representation learning
Federated Learning (FL) has emerged as a promising framework for distributed training of AI-
based services, applications, and network procedures in 6G. One of the major challenges …
based services, applications, and network procedures in 6G. One of the major challenges …
Relational deep reinforcement learning for routing in wireless networks
While routing in wireless networks has been studied extensively, existing protocols are
typically designed for a specific set of network conditions and so do not easily accommodate …
typically designed for a specific set of network conditions and so do not easily accommodate …
Deep reinforcement learning for scheduling in cellular networks
Integrating artificial intelligence (AI) into wireless networks has drawn significant interest in
both industry and academia. A common solution is to replace partial or even all modules in …
both industry and academia. A common solution is to replace partial or even all modules in …
A Safe Deep Reinforcement Learning Approach for Energy Efficient Federated Learning in Wireless Communication Networks
N Koursioumpas, L Magoula… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
Progressing towards a new era of Artificial Intelligence (AI)-enabled wireless networks,
concerns regarding the environmental impact of AI have been raised both in industry and …
concerns regarding the environmental impact of AI have been raised both in industry and …