A. Oliveira, Marcus Dias, Isabela Trindade, A. Klautau
{"title":"基于车辆和行人移动控制场景的光线追踪5G通道","authors":"A. Oliveira, Marcus Dias, Isabela Trindade, A. Klautau","doi":"10.14209/sbrt.2019.1570558978","DOIUrl":null,"url":null,"abstract":"Millimeter waves is one of 5G networks strategies to achieve high bit rates. Measurement campaigns with these signals are difficult and require expensive equipment. In order to generate realistic data this paper refines a methodology for virtual measurements of 5G channels, which combines a simulation of urban mobility with a ray-tracing simulator. The urban mobility simulator is responsible for controlling mobility, positioning pedestrians and vehicles throughout each scene while the ray-tracing simulator is repeatedly invoked, simulating the interactions between receivers and transmitters. The orchestration among both simulators is done using a Python software. To check how the realism can influence the computational cost, it was made a numerical analyze between the number of faces and the simulation time.","PeriodicalId":135552,"journal":{"name":"Anais de XXXVII Simpósio Brasileiro de Telecomunicações e Processamento de Sinais","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Ray-Tracing 5G Channels from Scenarios with Mobility Control of Vehicles and Pedestrians\",\"authors\":\"A. Oliveira, Marcus Dias, Isabela Trindade, A. Klautau\",\"doi\":\"10.14209/sbrt.2019.1570558978\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Millimeter waves is one of 5G networks strategies to achieve high bit rates. Measurement campaigns with these signals are difficult and require expensive equipment. In order to generate realistic data this paper refines a methodology for virtual measurements of 5G channels, which combines a simulation of urban mobility with a ray-tracing simulator. The urban mobility simulator is responsible for controlling mobility, positioning pedestrians and vehicles throughout each scene while the ray-tracing simulator is repeatedly invoked, simulating the interactions between receivers and transmitters. The orchestration among both simulators is done using a Python software. To check how the realism can influence the computational cost, it was made a numerical analyze between the number of faces and the simulation time.\",\"PeriodicalId\":135552,\"journal\":{\"name\":\"Anais de XXXVII Simpósio Brasileiro de Telecomunicações e Processamento de Sinais\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-01-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Anais de XXXVII Simpósio Brasileiro de Telecomunicações e Processamento de Sinais\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.14209/sbrt.2019.1570558978\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Anais de XXXVII Simpósio Brasileiro de Telecomunicações e Processamento de Sinais","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14209/sbrt.2019.1570558978","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Ray-Tracing 5G Channels from Scenarios with Mobility Control of Vehicles and Pedestrians
Millimeter waves is one of 5G networks strategies to achieve high bit rates. Measurement campaigns with these signals are difficult and require expensive equipment. In order to generate realistic data this paper refines a methodology for virtual measurements of 5G channels, which combines a simulation of urban mobility with a ray-tracing simulator. The urban mobility simulator is responsible for controlling mobility, positioning pedestrians and vehicles throughout each scene while the ray-tracing simulator is repeatedly invoked, simulating the interactions between receivers and transmitters. The orchestration among both simulators is done using a Python software. To check how the realism can influence the computational cost, it was made a numerical analyze between the number of faces and the simulation time.