Shiyuan Liu;Yujie Feng;Songjiang Yang;Bo Ma;Chuanhuang Li
{"title":"基于异构图神经网络的城市场景高效光线追踪框架","authors":"Shiyuan Liu;Yujie Feng;Songjiang Yang;Bo Ma;Chuanhuang Li","doi":"10.1109/LAWP.2025.3576376","DOIUrl":null,"url":null,"abstract":"In this letter, we propose an accurate and efficient heterogeneous graph neural network-powered ray tracing (HGNN-RT) framework to predict path loss for urban scenarios. The proposed HGNN-RT effectively captures both global and local features, which include broad path attributes, such as total path length and line-of-sight conditions, as well as specific interactions, such as reflection angles and obstacles. Moreover, due to the fast information flow of the graph neural networks, the HGNN-RT achieves efficiency-improved path loss prediction in complex multireflection and multipath propagation channels. Experimental results demonstrate that the proposed approach achieves errors within 2 dB of ray tracing benchmarks, while also being time-efficient and reliably generalizing to unseen environments for channel modeling.","PeriodicalId":51059,"journal":{"name":"IEEE Antennas and Wireless Propagation Letters","volume":"24 9","pages":"2884-2888"},"PeriodicalIF":4.8000,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Efficient Ray-Tracing Framework for Urban Scenarios Powered by Heterogeneous Graph Neural Networks\",\"authors\":\"Shiyuan Liu;Yujie Feng;Songjiang Yang;Bo Ma;Chuanhuang Li\",\"doi\":\"10.1109/LAWP.2025.3576376\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this letter, we propose an accurate and efficient heterogeneous graph neural network-powered ray tracing (HGNN-RT) framework to predict path loss for urban scenarios. The proposed HGNN-RT effectively captures both global and local features, which include broad path attributes, such as total path length and line-of-sight conditions, as well as specific interactions, such as reflection angles and obstacles. Moreover, due to the fast information flow of the graph neural networks, the HGNN-RT achieves efficiency-improved path loss prediction in complex multireflection and multipath propagation channels. Experimental results demonstrate that the proposed approach achieves errors within 2 dB of ray tracing benchmarks, while also being time-efficient and reliably generalizing to unseen environments for channel modeling.\",\"PeriodicalId\":51059,\"journal\":{\"name\":\"IEEE Antennas and Wireless Propagation Letters\",\"volume\":\"24 9\",\"pages\":\"2884-2888\"},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2025-06-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Antennas and Wireless Propagation Letters\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11023214/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Antennas and Wireless Propagation Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/11023214/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
An Efficient Ray-Tracing Framework for Urban Scenarios Powered by Heterogeneous Graph Neural Networks
In this letter, we propose an accurate and efficient heterogeneous graph neural network-powered ray tracing (HGNN-RT) framework to predict path loss for urban scenarios. The proposed HGNN-RT effectively captures both global and local features, which include broad path attributes, such as total path length and line-of-sight conditions, as well as specific interactions, such as reflection angles and obstacles. Moreover, due to the fast information flow of the graph neural networks, the HGNN-RT achieves efficiency-improved path loss prediction in complex multireflection and multipath propagation channels. Experimental results demonstrate that the proposed approach achieves errors within 2 dB of ray tracing benchmarks, while also being time-efficient and reliably generalizing to unseen environments for channel modeling.
期刊介绍:
IEEE Antennas and Wireless Propagation Letters (AWP Letters) is devoted to the rapid electronic publication of short manuscripts in the technical areas of Antennas and Wireless Propagation. These are areas of competence for the IEEE Antennas and Propagation Society (AP-S). AWPL aims to be one of the "fastest" journals among IEEE publications. This means that for papers that are eventually accepted, it is intended that an author may expect his or her paper to appear in IEEE Xplore, on average, around two months after submission.