{"title":"移动用户毫米波信号随机框架:实验、建模及其在波束跟踪中的应用","authors":"Yawen Fan, Jingchao Li, Husheng Li, Chao Tian","doi":"10.1109/GSMM.2018.8439186","DOIUrl":null,"url":null,"abstract":"In this paper, we propose to employ the Markov jump linear system to model the characteristics of millimeter wave (mmWave) signals, such as signal strength and angle-of-arrival (AoA), for mobile users. We integrate the spatial correlation between line-of-sight (LoS) and none-line-of-sight (NLoS) beams to the stochastic framework for better modeling mmWave. To handle the channel dynamics in mmWave communications, we derive a Markov gain-state channel model based on channel measurements in an indoor environment from our field measurement. The proposed stochastic model is used to estimate the transition probability of each channel. Then it is integrated into the Markov jump linear system framework to construct the linear estimator for channel variables, such as AoA and channel gain. The simulation result demonstrates the outperformance of the proposed framework against the previous Kalman-filtering-based tracking algorithm.","PeriodicalId":441407,"journal":{"name":"2018 11th Global Symposium on Millimeter Waves (GSMM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A Stochastic Framework of Millimeter Wave Signal for Mobile Users: Experiment, Modeling and Application in Beam Tracking\",\"authors\":\"Yawen Fan, Jingchao Li, Husheng Li, Chao Tian\",\"doi\":\"10.1109/GSMM.2018.8439186\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose to employ the Markov jump linear system to model the characteristics of millimeter wave (mmWave) signals, such as signal strength and angle-of-arrival (AoA), for mobile users. We integrate the spatial correlation between line-of-sight (LoS) and none-line-of-sight (NLoS) beams to the stochastic framework for better modeling mmWave. To handle the channel dynamics in mmWave communications, we derive a Markov gain-state channel model based on channel measurements in an indoor environment from our field measurement. The proposed stochastic model is used to estimate the transition probability of each channel. Then it is integrated into the Markov jump linear system framework to construct the linear estimator for channel variables, such as AoA and channel gain. The simulation result demonstrates the outperformance of the proposed framework against the previous Kalman-filtering-based tracking algorithm.\",\"PeriodicalId\":441407,\"journal\":{\"name\":\"2018 11th Global Symposium on Millimeter Waves (GSMM)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 11th Global Symposium on Millimeter Waves (GSMM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GSMM.2018.8439186\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 11th Global Symposium on Millimeter Waves (GSMM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GSMM.2018.8439186","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Stochastic Framework of Millimeter Wave Signal for Mobile Users: Experiment, Modeling and Application in Beam Tracking
In this paper, we propose to employ the Markov jump linear system to model the characteristics of millimeter wave (mmWave) signals, such as signal strength and angle-of-arrival (AoA), for mobile users. We integrate the spatial correlation between line-of-sight (LoS) and none-line-of-sight (NLoS) beams to the stochastic framework for better modeling mmWave. To handle the channel dynamics in mmWave communications, we derive a Markov gain-state channel model based on channel measurements in an indoor environment from our field measurement. The proposed stochastic model is used to estimate the transition probability of each channel. Then it is integrated into the Markov jump linear system framework to construct the linear estimator for channel variables, such as AoA and channel gain. The simulation result demonstrates the outperformance of the proposed framework against the previous Kalman-filtering-based tracking algorithm.