Graph Neural Network for Large-Scale Network Localization

Yan, Wenzhong; Jin, Di; Lin, Zhidi; Yin, Feng

doi:10.1109/icassp39728.2021.9414520

Cited by 27 publications

(7 citation statements)

References 24 publications

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“…For example, [9] developed a message passing algorithm for collaborative localisation of wireless sensor nodes, where ranging was performed with the help of the signal's Time of Arrival (ToA). [10] designed a graph neural network, which takes the adjacency matrix of network nodes as input, meaning it takes the layout of the network into account, where each edge between two nodes represents the distance between them. However, [10] did not take the angle of signal propagation into account in its adjacency matrix.…”

Section: Related Workmentioning

confidence: 99%

“…[10] designed a graph neural network, which takes the adjacency matrix of network nodes as input, meaning it takes the layout of the network into account, where each edge between two nodes represents the distance between them. However, [10] did not take the angle of signal propagation into account in its adjacency matrix. When it comes to studies that are more similar to our work, one example is WAIPO [11], a collaborative IPS for smartphones.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Positioning Vectors for Mobile Ad-Hoc Positioning

Sartayeva

Liu

et al. 2023

2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)

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In recent years, there has been considerable interest in indoor positioning with the advent of smartphones. Conventional indoor positioning methods are mostly infrastructure-based and non-collaborative. With the recent development of Ultra-WideBand (UWB) technologies, highly accurate distance and orientation detection have become available for supporting collaborative positioning, a new positioning paradigm. Hence there is a strong need to study collaborative positioning, which is referred to in this paper as mobile ad-hoc positioning (MAP). To contribute to the development of MAP, we present novel positioning vectors with the potential to tackle many related collaborative positioning problems and open an interesting area of research. Our contributions are outlined as follows. First, we present the concept of positioning vectors with the foundational features. Second, we present both experimental and simulation results, illustrating the use of positioning vectors. In particular, we discuss a COVID-19 related case study on social distancing. Last but not least, we discuss the future research directions of positioning vectors. In summary, this paper provides valuable insights into the development of MAP in general and positioning vectors in particular.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Positioning Vectors for Mobile Ad-Hoc Positioning

Sartayeva

Liu

et al. 2023

2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)

View full text Add to dashboard Cite

show abstract

“…Fourthly, there is a method using GNN [33], which is different from the previous methods that distinguish between NLoS and LoS. In this method, a GNN-based datadriven approach is used to achieve robust large-scale network localization and address mixed LoS/NLoS environments.…”

Section: Identifying Nlos Eventsmentioning

confidence: 99%

When Wireless Localization Meets Artificial Intelligence: Basics, Challenges, Synergies, and Prospects

Cha,

Lee,

Ozger

et al. 2023

Applied Sciences

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The rapid development of information communication and artificial intelligence (AI) technology is driving innovation in various new application fields such as autonomous driving, augmented reality, and the metaverse. In particular, the advancement of wireless localization technology plays a great role in these cutting-edge technologies. However, traditional wireless localization systems rely on the global navigation satellite system (GNSS), which is ineffective in indoor or underground environments. To overcome this issue, indoor positioning systems (IPS) have gained attention, and various localization techniques utilizing wireless communication were studied. Subsequently, AI technologies are improving the performance of wireless localization and addressing problems that were previously difficult to solve. In this paper, we summarize wireless localization techniques and define the factors that impede their performance. Furthermore, we categorize AI algorithms and present examples of how they can be used to address these hindering factors. Finally, we propose open research directions and prospects for AI-assisted wireless localization.

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“…W. Yan et al solved the network localization of a wireless network in two-dimensional (2-D) space using GCNs [78]. The adjacent matrix A T h ∈ R N ×N of an undirected graph corresponding a wireless network is constructed as follows…”

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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Graph Neural Network for Large-Scale Network Localization

Cited by 27 publications

References 24 publications

Positioning Vectors for Mobile Ad-Hoc Positioning

Positioning Vectors for Mobile Ad-Hoc Positioning

When Wireless Localization Meets Artificial Intelligence: Basics, Challenges, Synergies, and Prospects

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

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