mlsanityData to 14 Oct 2026

Fast Power Control Adaptation via Meta-Learning for Random Edge Graph Neural Networks

arXiv 2105.00459 ↗First posted 2 May 2021cs.IT cs.LG eess.SPcited by 8

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Abstract (arXiv)

Power control in decentralized wireless networks poses a complex stochastic optimization problem when formulated as the maximization of the average sum rate for arbitrary interference graphs. Recent work has introduced data-driven design methods that leverage graph neural network (GNN) to efficiently parametrize the power control policy mapping channel state information (CSI) to the power vector. The specific GNN architecture, known as random edge GNN (REGNN), defines a non-linear graph convolutional architecture whose spatial weights are tied to the channel coefficients, enabling a direct adaption to channel conditions. This paper studies the higher-level problem of enabling fast adaption of the power control policy to time-varying topologies. To this end, we apply first-order meta-learning on data from multiple topologies with the aim of optimizing for a few-shot adaptation to new network configurations.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 11 of its 21 references that are in the map. First posted on arXiv 2 May 2021.

Builds on

11 of 21 references.

References · 11

  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  2. 2018On First-Order Meta-Learning Algorithmscited by 1,018
  3. 2019Optimal Wireless Resource Allocation with Random Edge Graph Neural Networkscited by 90
  4. 2020Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocationcited by 61
  5. 2018A Very Brief Introduction to Machine Learning With Applications to Communication Systemscited by 35
  6. 2020Information-Theoretic Generalization Bounds for Meta-Learning and Applicationscited by 32
  7. 2019Learning to Demodulate from Few Pilots via Offline and Online Meta-Learningcited by 21
  8. 2020From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication Systemscited by 18
  9. 2019MIND: Model Independent Neural Decodercited by 11
  10. 2020Wireless Power Control via Counterfactual Optimization of Graph Neural Networkscited by 10
  11. 2014Evolution Towards 5G Multi-tier Cellular Wireless Networks: An Interference Management Perspectivecited by 1

Built on by

Cited by year

Citing papers per year: 2021: 4; 2022: 3; 2023: 0; 2024: 1.

8 papers.

  1. 2021Graph-based Deep Learning for Communication Networks: A Surveycited by 21
  2. 2022Learning Resilient Radio Resource Management Policies with Graph Neural Networkscited by 17
  3. 2021Modular Meta-Learning for Power Control via Random Edge Graph Neural Networkscited by 13
  4. 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
  5. 2021Wireless Link Scheduling via Graph Representation Learning: A Comparative Study of Different Supervision Levelscited by 4
  6. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 3
  7. 2021Bayesian Active Meta-Learning for Few Pilot Demodulation and Equalizationcited by 2
  8. 2022Weighted Sum-Rate Maximization With Causal Inference for Latent Interference Estimationcited by 0