mlsanityData to 14 Oct 2026

State-Augmented Learnable Algorithms for Resource Management in Wireless Networks

arXiv 2207.02242 ↗First posted 5 Jul 2022cs.LG cs.IT eess.SPcited by 14

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Abstract (arXiv)

We consider resource management problems in multi-user wireless networks, which can be cast as optimizing a network-wide utility function, subject to constraints on the long-term average performance of users across the network. We propose a state-augmented algorithm for solving the aforementioned radio resource management (RRM) problems, where, alongside the instantaneous network state, the RRM policy takes as input the set of dual variables corresponding to the constraints, which evolve depending on how much the constraints are violated during execution. We theoretically show that the proposed state-augmented algorithm leads to feasible and near-optimal RRM decisions. Moreover, focusing on the problem of wireless power control using graph neural network (GNN) parameterizations, we demonstrate the superiority of the proposed RRM algorithm over baseline methods across a suite of numerical experiments.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 30 of its 47 references that are in the map. First posted on arXiv 5 Jul 2022.

Builds on

30 of 47 references.

References · 30

  1. 2021Convex optimizationcited by 2,515
  2. 2017Deep Setscited by 1,421
  3. 2019Universal Invariant and Equivariant Graph Neural Networkscited by 183
  4. 2019ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representationscited by 156
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  8. 2019Optimal Wireless Resource Allocation with Random Edge Graph Neural Networkscited by 90
  9. 2020Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysiscited by 81
  10. 2018Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networkscited by 70
  11. 2020Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocationcited by 61
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18 more
  1. 2020Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learningcited by 42
  2. 2018Learning Optimal Resource Allocations in Wireless Systemscited by 40
  3. 2021State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning with Rewardscited by 40
  4. 2019Graph Embedding based Wireless Link Scheduling with Few Training Samplescited by 30
  5. 2019A Graph Neural Network Approach for Scalable Wireless Power Controlcited by 26
  6. 2021On the Universality of Graph Neural Networks on Large Random Graphscited by 26
  7. 2021Link Scheduling using Graph Neural Networkscited by 26
  8. 2021Learning Decentralized Wireless Resource Allocations with Graph Neural Networkscited by 25
  9. 2019Deep Learning based Wireless Resource Allocation with Application to Vehicular Networkscited by 20
  10. 2022Learning Resilient Radio Resource Management Policies with Graph Neural Networkscited by 17
  11. 2020Intelligent O-RAN for Beyond 5G and 6G Wireless Networkscited by 14
  12. 2021Modular Meta-Learning for Power Control via Random Edge Graph Neural Networkscited by 13
  13. 2020Wireless Power Control via Counterfactual Optimization of Graph Neural Networkscited by 10
  14. 2021Heterogeneous Transformer: A Scale Adaptable Neural Network Architecture for Device Activity Detectioncited by 10
  15. 2020Unsupervised Learning for Asynchronous Resource Allocation in Ad-hoc Wireless Networkscited by 9
  16. 2021Power Allocation for Wireless Federated Learning using Graph Neural Networkscited by 7
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  18. 2021Wireless Link Scheduling via Graph Representation Learning: A Comparative Study of Different Supervision Levelscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 1; 2025: 5; 2026: 4.

14 papers.

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  1. 2025Generative Diffusion Models for Resource Allocation in Wireless Networkscited by 6
  2. 2026Graph Signal Diffusion Models for Wireless Resource Allocationcited by 4
  3. 2025Opportunistic Routing in Wireless Communications via Learnable State-Augmented Policiescited by 3
  4. 2025Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regressioncited by 3
  5. 2024Learning to Slice Wi-Fi Networks: A State-Augmented Primal-Dual Approachcited by 2
  6. 2026Wireless Power Control Based on Large Language Modelscited by 2
  7. 2026A Graph Foundation Model for Wireless Resource Allocationcited by 2
  8. 2023Learning State-Augmented Policies for Information Routing in Communication Networkscited by 1
  9. 2025Power Allocation for Delay Optimization in Device-to-Device Networks: A Graph Reinforcement Learning Approachcited by 1
  10. 2022A State-Augmented Approach for Learning Optimal Resource Management Decisions in Wireless Networkscited by 0
  11. 2023A Deep Learning Based Resource Allocator for Communication Networks with Dynamic User Utility Demandscited by 0
  12. 2023Asynchronous Message-Passing and Zeroth-Order Optimization Based Distributed Learning with a Use-Case in Resource Allocation in Communication Networkscited by 0
2 more
  1. 2025QoS-Aware State-Augmented Learnable Framework for 5G NR-U/Wi-Fi Coexistence: Impact of Parameter Selection and Enhanced Collision Resolutioncited by 0
  2. 2026Unrolled Neural Networks for Constrained Optimizationcited by 0