mlsanityData to 14 Oct 2026

Learning Cellular Coverage from Real Network Configurations using GNNs

arXiv 2304.10328 ↗First posted 20 Apr 2023cs.NI cs.LGcited by 0

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Abstract (arXiv)

Cellular coverage quality estimation has been a critical task for self-organized networks. In real-world scenarios, deep-learning-powered coverage quality estimation methods cannot scale up to large areas due to little ground truth can be provided during network design & optimization. In addition they fall short in produce expressive embeddings to adequately capture the variations of the cells' configurations. To deal with this challenge, we formulate the task in a graph representation and so that we can apply state-of-the-art graph neural networks, that show exemplary performance. We propose a novel training framework that can both produce quality cell configuration embeddings for estimating multiple KPIs, while we show it is capable of generalising to large (area-wide) scenarios given very few labeled cells. We show that our framework yields comparable accuracy with models that have been trained using massively labeled samples.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 31 references that are in the map. First posted on arXiv 20 Apr 2023.

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References · 16

  1. 2018How Powerful are Graph Neural Networks?cited by 4,393
  2. 2019Strategies for Pre-training Graph Neural Networkscited by 971
  3. 2020GPT-GNN: Generative Pre-Training of Graph Neural Networkscited by 303
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  7. 2020Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysiscited by 81
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  9. 2018Spatial Deep Learning for Wireless Schedulingcited by 32
  10. 2020Lifelong Learning of Graph Neural Networks for Open-World Node Classificationcited by 22
  11. 2020Optimizing Coverage and Capacity in Cellular Networks using Machine Learningcited by 21
  12. 2021Scalable Power Control/Beamforming in Heterogeneous Wireless Networks with Graph Neural Networkscited by 14
4 more
  1. 2019Online Antenna Tuning in Heterogeneous Cellular Networks with Deep Reinforcement Learningcited by 13
  2. 2020Deep Learning-based Signal Strength Prediction Using Geographical Images and Expert Knowledgecited by 9
  3. 2021Deep Learning-based Power Control for Cell-Free Massive MIMO Networkscited by 4
  4. 2021A Graph Attention Learning Approach to Antenna Tilt Optimizationcited by 3

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