High Resolution Estimation of AoA, AoD and TdoA from MIMO Channel Sounding Measurements with Virtual Antenna Arrays: Maximum-Likelihood vs. Unitary Tensor-ESPRIT
{"title":"High Resolution Estimation of AoA, AoD and TdoA from MIMO Channel Sounding Measurements with Virtual Antenna Arrays: Maximum-Likelihood vs. Unitary Tensor-ESPRIT","authors":"S. Haefner, R. Thoma","doi":"10.11601/IJATES.V7I2.254","DOIUrl":null,"url":null,"abstract":"Estimating the parameters of a geometric propagation model from MIMO channel sounding measurements will be considered, which requires the solution of an inverse problem. Thus, a model of the measured data is derived, which incorporates a model of the measurement system as well as the parameters of interest. Based on the data model a maximum-likelihood estimator will be derived to infer the model parameters. Because virtual antenna arrays are considerer, formed by step-wise rotating directive antennas at transmitter and receiver side, the MIMO measurements are conducted in the beam-space. Hence, the data model can be described by a multidimensional convolution of the measurement system and the propagation channel. Based on the convolutional modelling, the parameter estimation problem is transformed into a harmonic retrieval problem, which can be solved by an Unitary Tensor-ESPRIT algorithm. The maximum-likelihood and ESPRIT estimator are compared by Monte-Carlo simulations according to their root-mean-square estimation error.","PeriodicalId":30494,"journal":{"name":"International Journal of Advances in Telecommunications Electrotechnics Signals and Systems","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Advances in Telecommunications Electrotechnics Signals and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11601/IJATES.V7I2.254","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Estimating the parameters of a geometric propagation model from MIMO channel sounding measurements will be considered, which requires the solution of an inverse problem. Thus, a model of the measured data is derived, which incorporates a model of the measurement system as well as the parameters of interest. Based on the data model a maximum-likelihood estimator will be derived to infer the model parameters. Because virtual antenna arrays are considerer, formed by step-wise rotating directive antennas at transmitter and receiver side, the MIMO measurements are conducted in the beam-space. Hence, the data model can be described by a multidimensional convolution of the measurement system and the propagation channel. Based on the convolutional modelling, the parameter estimation problem is transformed into a harmonic retrieval problem, which can be solved by an Unitary Tensor-ESPRIT algorithm. The maximum-likelihood and ESPRIT estimator are compared by Monte-Carlo simulations according to their root-mean-square estimation error.