Adaptive Wireless Power Allocation with Graph Neural Networks
Author:
Affiliation:
1. University of Pennsylvania
2. Intel Corporation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9745891/9746004/09746240.pdf?arnumber=9746240
Reference21 articles.
1. Graph Embedding-Based Wireless Link Scheduling With Few Training Samples
2. Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning
3. A Deep Reinforcement Learning Framework for Contention-Based Spectrum Sharing
4. Contrastive Self-Supervised Learning for Wireless Power Control
5. Fast Power Control Adaptation via Meta-Learning for Random Edge Graph Neural Networks
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1. Training a Graph Neural Network to solve URLLC and eMBB coexisting in 5G networks;Computer Communications;2024-09
2. Joint Admission and Power Control for Massive Connections via Graph Neural Network;IEEE Transactions on Vehicular Technology;2024-08
3. Multi-Agent Reinforcement Learning for Power Control in Wireless Networks via Adaptive Graphs;ICC 2024 - IEEE International Conference on Communications;2024-06-09
4. Energy efficient clustering for dense wireless sensor network by applying Graph Neural Networks with coverage metrics;Ad Hoc Networks;2024-04
5. Meta-Learning for Wireless Communications: A Survey and a Comparison to GNNs;IEEE Open Journal of the Communications Society;2024
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