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Graph neural network-based cell switching for energy optimization in ultra-dense heterogeneous networks.

Kang Tan1, Duncan Bremner2, Julien Le Kernec2

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This study introduces a Graph Neural Network-based Cell Switching Solution (GBCSS) for energy-efficient cellular networks. GBCSS offers significant energy savings and scalability for ultra-dense heterogeneous networks.

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Area of Science:

  • Telecommunications Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Ultra-dense heterogeneous networks (HetNets) face escalating energy consumption due to large-scale base station (BS) deployments.
  • Achieving energy efficiency in cellular networks is crucial for reducing operational costs and promoting environmental sustainability.
  • Traditional cell switching algorithms struggle with computational demands and generalization in complex HetNets.

Purpose of the Study:

  • To propose and evaluate a novel Graph Neural Network-based Cell Switching Solution (GBCSS) for enhancing energy efficiency in ultra-dense HetNets.
  • To address the limitations of existing heuristic and learning-based cell switching methods.
  • To demonstrate the adaptability and performance of GBCSS across various network conditions.

Main Methods:

  • Development of a GNN-based cell switching algorithm (GBCSS) designed for reduced computational complexity.
  • Performance evaluation using the Milan telecommunication dataset, comprising real-world call detail records.
  • Comparative analysis of GBCSS against an exhaustive search (ES) algorithm, a state-of-the-art learning-based algorithm, and a baseline network without cell switching.

Main Results:

  • GBCSS achieved a 10.41% energy efficiency gain compared to the baseline.
  • The proposed solution reached 75.76% of the optimal performance achieved by the exhaustive search algorithm.
  • Demonstrated significant scalability and generalization capabilities across different load conditions and BS densities.

Conclusions:

  • GBCSS presents a computationally efficient and effective approach for adaptive cell switching in ultra-dense HetNets.
  • The GNN-based method shows strong potential for improving cellular network energy efficiency and sustainability.
  • GBCSS is well-suited for practical deployment in next-generation, ultra-dense cellular network infrastructures.