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A State-Augmented Approach for Learning Optimal Resource Management Decisions in Wireless Networks

arXiv 2210.16412 ↗First posted 28 Oct 2022cs.LG cs.IT eess.SPcited by 0

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Abstract (arXiv)

We consider a radio resource management (RRM) problem in a multi-user wireless network, where the goal is to optimize a network-wide utility function subject to constraints on the ergodic average performance of users. We propose a state-augmented parameterization for the RRM policy, where alongside the instantaneous network states, the RRM policy takes as input the set of dual variables corresponding to the constraints. We provide theoretical justification for the feasibility and near-optimality of the RRM decisions generated by the proposed state-augmented algorithm. Focusing on the power allocation problem with RRM policies parameterized by a graph neural network (GNN) and dual variables sampled from the dual descent dynamics, we numerically demonstrate that the proposed approach achieves a superior trade-off between mean, minimum, and 5th percentile rates than baseline methods.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 27 references that are in the map. First posted on arXiv 28 Oct 2022.

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References · 20

  1. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
  2. 2019Optimal Wireless Resource Allocation with Random Edge Graph Neural Networkscited by 90
  3. 2020Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysiscited by 80
  4. 2018Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networkscited by 70
  5. 2020Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocationcited by 61
  6. 2020Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networkscited by 45
  7. 2020Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learningcited by 41
  8. 2018Learning Optimal Resource Allocations in Wireless Systemscited by 39
  9. 2021State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning with Rewardscited by 37
  10. 2019Graph Embedding based Wireless Link Scheduling with Few Training Samplescited by 30
  11. 2019A Graph Neural Network Approach for Scalable Wireless Power Controlcited by 26
  12. 2021Link Scheduling using Graph Neural Networkscited by 26
8 more
  1. 2022A Lagrangian Duality Approach to Active Learningcited by 24
  2. 2019Deep Learning based Wireless Resource Allocation with Application to Vehicular Networkscited by 19
  3. 2022Learning Resilient Radio Resource Management Policies with Graph Neural Networkscited by 17
  4. 2020Intelligent O-RAN for Beyond 5G and 6G Wireless Networkscited by 14
  5. 2021Modular Meta-Learning for Power Control via Random Edge Graph Neural Networkscited by 13
  6. 2022State-Augmented Learnable Algorithms for Resource Management in Wireless Networkscited by 13
  7. 2020Unsupervised Learning for Asynchronous Resource Allocation in Ad-hoc Wireless Networkscited by 9
  8. 2021Power Allocation for Wireless Federated Learning using Graph Neural Networkscited by 7

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