mlsanityData to 14 Oct 2026

Wireless Link Scheduling via Graph Representation Learning: A Comparative Study of Different Supervision Levels

arXiv 2110.01722 ↗First posted 4 Oct 2021eess.SP cs.IT cs.LGcited by 4

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Abstract (arXiv)

We consider the problem of binary power control, or link scheduling, in wireless interference networks, where the power control policy is trained using graph representation learning. We leverage the interference graph of the wireless network as an underlying topology for a graph neural network (GNN) backbone, which converts the channel matrix to a set of node embeddings for all transmitter-receiver pairs. We show how the node embeddings can be trained in several ways, including via supervised, unsupervised, and self-supervised learning, and we compare the impact of different supervision levels on the performance of these methods in terms of the system-level throughput, convergence behavior, sample efficiency, and generalization capability.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 16 of its 22 references that are in the map. First posted on arXiv 4 Oct 2021.

Builds on

16 of 22 references.

References · 16

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  2. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  3. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  4. 2019ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representationscited by 156
  5. 2019Stability Properties of Graph Neural Networkscited by 127
  6. 2019Optimal Wireless Resource Allocation with Random Edge Graph Neural Networkscited by 90
  7. 2020Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysiscited by 81
  8. 2018Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networkscited by 70
  9. 2020Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learningcited by 42
  10. 2018Learning Optimal Resource Allocations in Wireless Systemscited by 40
  11. 2019Graph Embedding based Wireless Link Scheduling with Few Training Samplescited by 30
  12. 2019A Graph Neural Network Approach for Scalable Wireless Power Controlcited by 26
4 more
  1. 2020Wireless Power Control via Counterfactual Optimization of Graph Neural Networkscited by 10
  2. 2021Fast Power Control Adaptation via Meta-Learning for Random Edge Graph Neural Networkscited by 8
  3. 2021AI Empowered Resource Management for Future Wireless Networkscited by 6
  4. 2020Contrastive Self-Supervised Learning for Wireless Power Controlcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 0; 2024: 0; 2025: 0; 2026: 2.

4 papers.

  1. 2022Learning Resilient Radio Resource Management Policies with Graph Neural Networkscited by 17
  2. 2022State-Augmented Learnable Algorithms for Resource Management in Wireless Networkscited by 14
  3. 2026Priority-Aware Learning-Unlearning Correction for Dynamic Decentralized LoRA Fine-Tuningcited by 0
  4. 2026AI-Driven Real-Time Relay Optimisation in Smart Urban NR-V2X Networks via Learning-to-Optimise Graph Neural Networkscited by 0