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Accurate Localization in LOS/NLOS Channel Coexistence Scenarios Based on Heterogeneous Knowledge Graph Inference
Abstract
Accurate localization is one of the basic requirements for smart cities and smart factories. In wireless cellular network localization, the straight-line propagation of electromagnetic waves between base stations and users is called line-of-sight (LOS) wireless propagation. In some cases, electromagnetic wave signals cannot propagate in a straight line due to obstruction by buildings or trees, and these scenarios are usually called non-LOS (NLOS) wireless propagation. Traditional localization algorithms such as TDOA, AOA, etc., are based on LOS channels, which are no longer applicable in environments where NLOS propagation is dominant, and in most scenarios, the number of base stations with LOS channels containing users is often small, resulting in traditional localization algorithms being unable to satisfy the accuracy demand of high-precision localization. In addition, some nonideal factors may be included in the actual system, all of which can lead to localization accuracy degradation. Therefore, the approach developed in this paper uses knowledge graph and graph neural network (GNN) technology to model communication data as knowledge graphs, and it adopts the knowledge graph inference technique based on a heterogeneous graph attention mechanism to infer unknown data representations in complex scenarios based on the known data and the relationships between the data to achieve high-precision localization in scenarios with LOS/NLOS channel coexistence. We experimentally demonstrate a spatial 2D localization accuracy level of approximately 10 meters on multiple datasets and find that our proposed algorithm has higher accuracy and stronger robustness than the state-of-the-art algorithms.
References
[1]
Jacek Stefański. 2009. Hyperbolic position location estimation in the multipath propagation environment. In Proc. of IFIP. Springer, 232–239.
[2]
Hing Cheung So and Estella Man Kit Shiu. 2003. Performance of TOA-AOA hybrid mobile location. IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences 86, 8 (2003), 2136–2138.
[3]
Yiu Tong Chan and KC Ho. 1994. A simple and efficient estimator for hyperbolic location. IEEE transactions on signal processing 42, 8 (1994), 1905–1915.
[4]
Paramvir Bahl and Venkata N Padmanabhan. 2000. RADAR: An in-building RF-based user location and tracking system. In Proc. of IEEE INFOCOM, Vol. 2. 775–784.
[5]
Ye Tian, Bruce Denby, Iness Ahriz, Pierre Roussel, and Gérard Dreyfus. 2015. Robust indoor localization and tracking using GSM fingerprints. EURASIP Journal on Wireless Communications and Networking 2015, 1(2015), 1–12.
[6]
Sandy Mahfouz, Farah Mourad-Chehade, Paul Honeine, Joumana Farah, and Hichem Snoussi. 2015. Kernel-based machine learning using radio-fingerprints for localization in wsns. IEEE Trans. Aerospace Electron. Systems 51, 2 (2015), 1324–1336.
[7]
Alex Varshavsky, Eyal De Lara, Jeffrey Hightower, Anthony LaMarca, and Veljo Otsason. 2007. GSM indoor localization. Pervasive and mobile computing 3, 6 (2007), 698–720.
[8]
Hamada Rizk, Marwan Torki, and Moustafa Youssef. 2018. CellinDeep: Robust and accurate cellular-based indoor localization via deep learning. IEEE Sensors Journal 19, 6 (2018), 2305–2312.
[9]
Hamada Rizk, Moustafa Abbas, and Moustafa Youssef. 2020. Omnicells: Cross-device cellular-based indoor location tracking using deep neural networks. In Proc. of IEEE PerCom. 1–10.
[10]
Max Welling and Thomas N Kipf. 2016. Semi-Supervised Classification with Graph Convolutional Networks. In Proc. of ICLR.
[11]
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. (2017), 1025–1035.
[12]
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph Attention Networks. Stat 1050(2017), 20.
[13]
Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, and Nitesh V Chawla. 2019. Heterogeneous graph neural network. In Proc of ACM SIGKDD. 793–803.
[14]
Colin Lea, Michael D Flynn, Rene Vidal, Austin Reiter, and Gregory D Hager. 2017. Temporal convolutional networks for action segmentation and detection. In Proc. of IEEE CVPR. 156–165.
[15]
Philippe G Ciarlet. 2013. Linear and Nonlinear Functional Analysis with Applications. Vol. 130. Siam.
[16]
Elias M Stein and Rami Shakarchi. 2009. Real Analysis: Measure Theory, Integration, and Hilbert Spaces. Princeton University Press.
[17]
Federico Monti, Karl Otness, and Michael M Bronstein. 2018. Motifnet: A Motif-Based Graph Convolutional Network for Directed Graphs. In Proc. of IEEE DSW. 225–228.
[18]
Qun Niu, Kunxin Zhu, Suining He, Shaoqi Cen, S-H Gary Chan, and Ning Liu. 2023. VILL: Towards Efficient and Automatic Visual Landmark Labeling. ACM Transactions on Sensor Networks(2023).
[19]
Carlo Tomasi and Roberto Manduchi. 1998. Bilateral filtering for gray and color images. In Proc. of IEEE ICCV. 839–846.
[20]
X. Zhou, X. Zheng, X. Cui, J. Shi, W. Liang, Z. Yan, L.T. Yang, S. Shimizu, I. Kevin, and K. Wang. 2023. Digital Twin Enhanced Federated Reinforcement Learning with Lightweight Knowledge Distillation in Mobile Networks. IEEE Journal on Selected Areas in Communications (2023).
[21]
Jie Hu, Li Shen, and Gang Sun. 2018. Squeeze-and-excitation networks. In Proc. of IEEE CVPR. 7132–7141.
[22]
Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S Yu. 2019. Heterogeneous graph attention network. In Proc of WWW. 2022–2032.
[23]
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-Term Memory. Neural Computation 9, 8 (1997), 1735–1780.
[24]
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In Proc of IEEE CVPR. 1–9.
[25]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In Proc. of IEEE CVPR. 770–778.
[26]
Xu Chen, Junshan Wang, and Kunqing Xie. 2021. TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learning. In Proc. of IJCAI. 1–11.
[27]
Duncan J Watts and Steven H Strogatz. 1998. Collective Dynamics of ‘Small-World’ Networks. Nature 393, 6684 (1998), 440–442.
[28]
Mai Ibrahim, Marwan Torki, and Mustafa ElNainay. 2018. CNN based indoor localization using RSS time-series. In 2018 IEEE symposium on computers and communications (ISCC). IEEE, 01044–01049.
[29]
Moustafa Abbas, Moustafa Elhamshary, Hamada Rizk, Marwan Torki, and Moustafa Youssef. 2019. WiDeep: WiFi-based accurate and robust indoor localization system using deep learning. In proc of PerCom. IEEE, 1–10.
[30]
Jing Gao, Peng Li, Zhikui Chen, and Jianing Zhang. 2020. A survey on deep learning for multimodal data fusion. Neural Computation 32, 5 (2020), 829–864.
[31]
Yikai Wang, Wenbing Huang, Fuchun Sun, Tingyang Xu, Yu Rong, and Junzhou Huang. 2020. Deep multimodal fusion by channel exchanging. Advances in neural information processing systems 33 (2020), 4835–4845.
[32]
JQ James. 2022. Graph construction for traffic prediction: a data-driven approach. IEEE Transactions on Intelligent Transportation Systems 23, 9(2022), 15015–15027.
[33]
Amir Markovitz, Gilad Sharir, Itamar Friedman, Lihi Zelnik-Manor, and Shai Avidan. 2020. Graph embedded pose clustering for anomaly detection. In Proc of IEEE CVPR. 10539–10547.
[34]
Weiqi Zhang, Chen Zhang, and Fugee Tsung. 2022. Grelen: Multivariate time series anomaly detection from the perspective of graph relational learning. In Proc of IJCAI. 2390–2397.
[35]
Julian Nubert, Shehryar Khattak, and Marco Hutter. 2022. Graph-based multi-sensor fusion for consistent localization of autonomous construction robots. In Proc of IEEE ICRA. 10048–10054.
[36]
Trisha Mittal, Uttaran Bhattacharya, Rohan Chandra, Aniket Bera, and Dinesh Manocha. 2020. M3er: Multiplicative multimodal emotion recognition using facial, textual, and speech cues. In Proc of AAAI, Vol. 34. 1359–1367.
[37]
Devamanyu Hazarika, Roger Zimmermann, and Soujanya Poria. 2020. Misa: Modality-invariant and-specific representations for multimodal sentiment analysis. In Proc of ACM MM. 1122–1131.
[38]
Bojun Zhang, Mengning Li, Xin Xie, Luoyi Fu, Xinyu Tong, and Xiulong Liu. 2022. RC6D: An RFID and CV Fusion System for Real-time 6D Object Pose Estimation. In Proc of IEEE INFOCOM. 690–699.
[39]
Qingsong Lv, Ming Ding, Qiang Liu, Yuxiang Chen, Wenzheng Feng, Siming He, Chang Zhou, Jianguo Jiang, Yuxiao Dong, and Jie Tang. 2021. Are we really making much progress? revisiting, benchmarking and refining heterogeneous graph neural networks. In Proc of ACM SIGKDD. 1150–1160.
[40]
X. Zhou, W. Liang, I. Kevin, K. Wang, and L.T. Yang. 2020. Deep correlation mining based on hierarchical hybrid networks for heterogeneous big data recommendations. IEEE Transactions on Computational Social Systems 8, 1 (2020), 171–178.
[41]
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. 2018. Modeling relational data with graph convolutional networks. In Proc of ESWC. Springer, 593–607.
[42]
Xinyu Fu, Jiani Zhang, Ziqiao Meng, and Irwin King. 2020. Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding. In Proc of WWW. 2331–2341.
[43]
Ziniu Hu, Yuxiao Dong, Kuansan Wang, and Yizhou Sun. 2020. Heterogeneous graph transformer. In Proc of WWW. 2704–2710.
[44]
X. Zhou, X. Ye, K. Wang, W. Liang, N.K.C. Nair, S. Shimizu, Z. Yan, and Q. Jin. 2023. Hierarchical Federated Learning With Social Context Clustering-Based Participant Selection for Internet of Medical Things Applications. IEEE Transactions on Computational Social Systems (Apr. 2023).
[45]
Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. 2019. Kgat: Knowledge graph attention network for recommendation. In Proc of ACM SIGKDD. 950–958.
[46]
Linsong Cheng and Jiliang Wang. 2016. How can I guard my AP? Non-intrusive user identification for mobile devices using WiFi signals. In Proc. of ACM MobiHoc. 91–100.
[47]
Xiaolong Zheng, Jiliang Wang, Longfei Shangguan, Zimu Zhou, and Yunhao Liu. 2016. Smokey: Ubiquitous smoking detection with commercial WiFi infrastructures. In Proc. of IEEE INFOCOM. IEEE, 1–9.
[48]
Leonardo L de Oliveira, Gabriel H Eisenkraemer, Everton A Carara, João B Martins, and José Monteiro. 2022. Mobile Localization Techniques for Wireless Sensor Networks: Survey and Recommendations. ACM Transactions on Sensor Networks (TOSN)(2022).
[49]
X. Zhou, W. Liang, K. Wang, Z. Yan, L.T. Yang, W. Wei, J. Ma, and Q. Jin. 2023. Decentralized P2P Federated Learning for Privacy-Preserving and Resilient Mobile Robotic Systems. IEEE Wireless Communications 30, 2 (Apr. 2023), 82–89.
[50]
X. Zhou, X. Zheng, T. Shu, W. Liang, K. Wang, L. Qi, S. Shimizu, and Q. Jin. 2023. Information Theoretic Learning-Enhanced Dual-Generative Adversarial Networks With Causal Representation for Robust OOD Generalization.IEEE Transactions on Neural Networks and Learning Systems (2023).
[51]
Lionel M Ni, Yunhao Liu, Yiu Cho Lau, and Abhishek P Patil. 2003. LANDMARC: Indoor Location Sensing Using Active RFID. In Proc. of IEEE PerCom. 407–415.
[52]
Nian Min, Xiang Zhengheng, and Jin Lu. 2022. Cluster Analysis of mobile communication network station location. In Proc. of ICIIP. 1–6.
[53]
Chenyu Hou, Bin Cao, Sijie Ruan, and Jing Fan. 2021. TLDS: A Transfer-Learning-Based Delivery Station Location Selection Pipeline. ACM Transactions on Intelligent Systems and Technology (TIST) 12, 4(2021), 1–24.
[54]
W. Liang, Y. Hu, X. Zhou, Y. Pan, and K. Wang. 2022. Variational Few-Shot Learning for Microservice-Oriented Intrusion Detection in Distributed Industrial IoT. IEEE Transactions on Industrial Informatics 18, 8(Aug. 2022), 5087–5095.
[55]
Guimin Dong, Mingyue Tang, Zhiyuan Wang, Jiechao Gao, Sikun Guo, Lihua Cai, Robert Gutierrez, Bradford Campbell, Laura E Barnes, and Mehdi Boukhechba. 2022. Graph neural networks in IoT: A survey. ACM Transactions on Sensor Networks (TOSN)(2022).
[56]
Zhan Gao and Deniz Gunduz. 2023. AirGNN: Graph Neural Network over the Air. arXiv preprint arXiv:2302.08447(2023).
[57]
Hang Zhao, Yujing Wang, Juanyong Duan, Congrui Huang, Defu Cao, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, and Qi Zhang. 2020. Multivariate Time-Series Anomaly Detection Via Graph Attention Network. In Proc. of IEEE ICDM. 841–850.
[58]
Zhan Gao, Mark Eisen, and Alejandro Ribeiro. 2020. Resource allocation via graph neural networks in free space optical fronthaul networks. In Proc. of IEEE GLOBECOM. IEEE, 1–6.
[59]
Ailin Deng and Bryan Hooi. 2021. Graph Neural Network-Based Anomaly Detection in Multivariate Time Series. In Proc. of AAAI. 4027–4035.
[60]
Zhan Gao, Yulin Shao, Deniz Gunduz, and Amanda Prorok. 2022. Decentralized Channel Management in WLANs with Graph Neural Networks. In Proc. of IEEE CCNC.
[61]
Mingyue Tang, Guimin Dong, Jamie Zoellner, Brendan Bowman, Emaad Abel-Rahman, and Mehdi Boukhechba. 2022. Using Ubiquitous Mobile Sensing and Temporal Sensor-Relation Graph Neural Network to Predict Fluid Intake of End Stage Kidney Patients. In Proc. of ACM IPSN. IEEE, 298–309.
[62]
Junru Chen, Yang Yang, Tao Yu, Yingying Fan, Xiaolong Mo, and Carl Yang. 2022. BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning. In Proc. of ACM SIGKDD. 2741–2751.
Index Terms
- Accurate Localization in LOS/NLOS Channel Coexistence Scenarios Based on Heterogeneous Knowledge Graph Inference
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Publication History
Online AM: 07 March 2024
Accepted: 25 February 2024
Revised: 29 December 2023
Received: 15 March 2023
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