An Overview on the Application of Graph Neural Networks in Wireless Networks

He, Silu; Xiong, Shaowen; Ou, Yang; Zhang, J.; Wang, Jin; Huang, Yongming; Zhang, Y.

doi:10.48550/arxiv.2107.03029

Cited by 5 publications

(4 citation statements)

References 105 publications

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“…All BSs are assumed to have the same maximum capacity in terms of radio resources (bandwidth and resource blocks) to simplify the traffic load normalization during the data pre-processing, and the calculations in Eqs. (12) and (21). The reason is that we are only interested in whether the original traffic load is preserved for each cell-switching scheme according to the introduced performance metrics, following the optimization constraint defined by Eq.…”

Section: Evaluation Configurationsmentioning

confidence: 99%

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

Tan

Bremner

Kernec

et al. 2022

Sci Rep

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The development of ultra-dense heterogeneous networks (HetNets) will cause a significant rise in energy consumption with large-scale base station (BS) deployments, requiring cellular networks to be more energy efficient to reduce operational expense and promote sustainability. Cell switching is an effective method to achieve the energy efficiency goals, but traditional heuristic cell switching algorithms are computationally demanding with limited generalization abilities for ultra-dense HetNet applications, motivating the usage of machine learning techniques for adaptive cell switching. Graph neural networks (GNNs) are powerful deep learning models with strong generalization abilities but receive little attention for cell switching. This paper proposes a GNN-based cell switching solution (GBCSS) that has a smaller computational complexity than existing heuristic algorithms. The presented performance evaluation uses the Milan telecommunication dataset based on real-world call detail records, comparing GBCSS with a traditional exhaustive search (ES) algorithm, a state-of-the-art learning-based algorithm, and the baseline without cell switching. Results indicate that GBCSS achieves a 10.41% energy efficiency gain when compared with the baseline and achieves 75.76% of the optimal performance obtained with ES algorithm. The results also demonstrate GBCSS’ significant scalability and generalization abilities to differing load conditions and the number of BSs, suggesting this approach is well-suited to ultra-dense HetNet deployment.

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Section: Evaluation Configurationsmentioning

confidence: 99%

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

Tan

Bremner

Kernec

et al. 2022

Sci Rep

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show abstract

“…Due to their localized nature, GNNs have also been applied to cooperative [30] and decentralized [31] control problems in networked systems. A review of the use of GNNs in wireless communication can be found in [32].…”

Section: Prior Workmentioning

confidence: 99%

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

Nikoloska¹,

Simeone²

2021

Preprint

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In this paper, we consider the problem of power control for a wireless network with an arbitrarily time-varying topology, including the possible addition or removal of nodes. A data-driven design methodology that leverages graph neural networks (GNNs) is adopted in order to efficiently parametrize the power control policy mapping the channel state information (CSI) to transmit powers. The specific GNN architecture, known as random edge GNN (REGNN), defines a non-linear graph convolutional filter whose spatial weights are tied to the channel coefficients. While prior work assumed a joint training approach whereby the REGNN-based policy is shared across all topologies, this paper targets adaptation of the power control policy based on limited CSI data regarding the current topology. To this end, we propose both black-box and modular meta-learning techniques. Black-box meta-learning optimizes a general-purpose adaptation procedure via (stochastic) gradient descent, while modular meta-learning finds a set of reusable modules that can form components of a solution for any new network topology.Numerical results validate the benefits of meta-learning for power control problems over joint training schemes, and demonstrate the advantages of modular meta-learning when data availability is extremely limited.

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“…Over the past few years, machine learning, and particularly deep learning, architectures have been increasingly used to address challenging problems in wireless communications, including those that fall under the umbrella of radio resource management (RRM), such as beamforming and power control [1][2][3]. More recently, solutions based on graph neural networks (GNNs), or in general, graph representation learning, have become more popular, mainly due to their desirable properties, such as permutation equivariance, size invariance, and stability to perturbations [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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

Naderializadeh¹

2021

Preprint

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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 selfsupervised 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. 1

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An Overview on the Application of Graph Neural Networks in Wireless Networks

Cited by 5 publications

References 105 publications

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

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

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

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

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