Decentralized Inference with Graph Neural Networks in Wireless Communication Systems

Lee, Mengyuan; Yu, Guanding; Dai, Huaiyu

doi:10.48550/arxiv.2104.09027

Cited by 5 publications

(6 citation statements)

References 42 publications

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“…In addition to power allocation [9]- [12], GNNs have been used to address cellular [25] and satellite [26] traffic prediction, link scheduling [27], channel control [28], and localization [29]. 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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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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“…Fortunately, similar to the discussion in the last subsection, the optimal distributed algorithm can be learned automatically. To meet the distributed requirement, specialized neural network architectures, e.g., GNNs, should be adopted [5], [7], [21], [22].…”

Section: B Automatic Design Of Distributed Algorithmsmentioning

confidence: 99%

AI Empowered Resource Management for Future Wireless Networks

Shen¹,

Zhang²,

Song³

et al. 2021

Preprint

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Resource management plays a pivotal role in wireless networks, which, unfortunately, leads to challenging NP-hard problems. Artificial Intelligence (AI), especially deep learning techniques, has recently emerged as a disruptive technology to solve such challenging problems in a real-time manner. However, although promising results have been reported, practical design guidelines and performance guarantees of AI-based approaches are still missing. In this paper, we endeavor to address two fundamental questions: 1) What are the main advantages of AI-based methods compared with classical techniques; and 2) Which neural network should we choose for a given resource management task. For the first question, four advantages are identified and discussed. For the second question, optimality gap, i.e., the gap to the optimal performance, is proposed as a measure for selecting model architectures, as well as, for enabling a theoretical comparison between different AI-based approaches. Specifically, for K-user interference management problem, we theoretically show that graph neural networks (GNNs) are superior to multi-layer perceptrons (MLPs), and the performance gap between these two methods grows with √ K.

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“…T h . The initialization is H (0) = X, where X = A T h ⊙ X. M. Lee et al analyzed and enhanced the robustness of the decentralized GNN in different wireless communication systems, making the prediction results not only accurate but also robust to transmission errors [79].…”

Section: G Othersmentioning

confidence: 99%

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

He¹,

Xiong²,

Ou³

et al. 2021

Preprint

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With the rapid enhancement of computer computing power, deep learning methods, e.g., convolution neural networks, recurrent neural networks, etc., have been applied in wireless network widely and achieved impressive performance. In recent years, in order to mine the topology information of graphstructured data in wireless network as well as contextual information, graph neural networks have been introduced and have achieved the state-of-the-art performance of a series of wireless network problems. In this review, we first simply introduce the progress of several classical paradigms, such as graph convolutional neural networks, graph attention networks, graph autoencoder, graph recurrent networks, graph reinforcement learning and spatial-temporal graph neural networks, of graph neural networks comprehensively. Then, several applications of graph neural networks in wireless networks such as power control, link scheduling, channel control, wireless traffic prediction, vehicular communication, point cloud, etc., are discussed in detail. Finally, some research trends about the applications of graph neural networks in wireless networks are discussed.

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Decentralized Inference with Graph Neural Networks in Wireless Communication Systems

Cited by 5 publications

References 42 publications

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

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

AI Empowered Resource Management for Future Wireless Networks

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

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