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

arXiv: https://arxiv.org/abs/2105.00459 · HTML: https://mlsanity.com/paper/2105.00459/

## 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 (most cited first, 11 of 11)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [Optimal Wireless Resource Allocation with Random Edge Graph Neural Networks](https://mlsanity.com/paper/1909.01865/) (2019)
- [Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocation](https://mlsanity.com/paper/2009.10812/) (2020)
- [A Very Brief Introduction to Machine Learning With Applications to Communication Systems](https://mlsanity.com/paper/1808.02342/) (2018)
- [Information-Theoretic Generalization Bounds for Meta-Learning and Applications](https://mlsanity.com/paper/2005.04372/) (2020)
- [Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning](https://mlsanity.com/paper/1908.09049/) (2019)
- [From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication Systems](https://mlsanity.com/paper/2001.01227/) (2020)
- [MIND: Model Independent Neural Decoder](https://mlsanity.com/paper/1903.02268/) (2019)
- [Wireless Power Control via Counterfactual Optimization of Graph Neural Networks](https://mlsanity.com/paper/2002.07631/) (2020)
- [Evolution Towards 5G Multi-tier Cellular Wireless Networks: An Interference Management Perspective](https://mlsanity.com/paper/1401.5530/) (2014)

## Built on by (most cited first, 8 of 8)

- [Graph-based Deep Learning for Communication Networks: A Survey](https://mlsanity.com/paper/2106.02533/) (2021)
- [Learning Resilient Radio Resource Management Policies with Graph Neural Networks](https://mlsanity.com/paper/2203.11012/) (2022)
- [Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks](https://mlsanity.com/paper/2108.13178/) (2021)
- [Federated Learning and Meta Learning: Approaches, Applications, and Directions](https://mlsanity.com/paper/2210.13111/) (2022)
- [Wireless Link Scheduling via Graph Representation Learning: A Comparative Study of Different Supervision Levels](https://mlsanity.com/paper/2110.01722/) (2021)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [Bayesian Active Meta-Learning for Few Pilot Demodulation and Equalization](https://mlsanity.com/paper/2108.00785/) (2021)
- [Weighted Sum-Rate Maximization With Causal Inference for Latent Interference Estimation](https://mlsanity.com/paper/2211.08327/) (2022)

## 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.