A 3D Space-Time-Frequency Non-Stationary Model for Low-Altitude UAV mmWave and Massive MIMO Aerial Fading Channels
In this paper, a three-dimensional (3D) geometry-based stochastic model (GBSM) is proposed for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) unmanned aerial vehicle (UAV) channels. The proposed model is the first mmWave massive MIMO UAV two-cylinder GBSM that enables to join...
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description | In this paper, a three-dimensional (3D) geometry-based stochastic model (GBSM) is proposed for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) unmanned aerial vehicle (UAV) channels. The proposed model is the first mmWave massive MIMO UAV two-cylinder GBSM that enables to jointly model the channel space-time-frequency non-stationarity by a novel variable parameter-based method. In this novel method, key parameters of UAV channels are assumed to vary in space, time, and frequency domains, and the effect of the unique UAV-related parameters is further taken into account, such as the UAV's altitude, velocity, and moving directions. Based on the proposed model, some statistical properties are derived, including the time-variant transfer function (TVTF), space-time-frequency correlation function (STF-CF), Doppler power spectral density (PSD), and the standard deviation of Doppler frequency on antenna arrays. Simulation results show that the channel non-stationarity in space, time, and frequency domains can be captured, and the aforementioned UAV channel-related parameters have a great impact on channel statistics. Finally, the utility of the proposed model is validated by the excellent agreement between simulation results and measurements. |
doi_str_mv | 10.1109/TAP.2022.3209192 |
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The proposed model is the first mmWave massive MIMO UAV two-cylinder GBSM that enables to jointly model the channel space-time-frequency non-stationarity by a novel variable parameter-based method. In this novel method, key parameters of UAV channels are assumed to vary in space, time, and frequency domains, and the effect of the unique UAV-related parameters is further taken into account, such as the UAV's altitude, velocity, and moving directions. Based on the proposed model, some statistical properties are derived, including the time-variant transfer function (TVTF), space-time-frequency correlation function (STF-CF), Doppler power spectral density (PSD), and the standard deviation of Doppler frequency on antenna arrays. Simulation results show that the channel non-stationarity in space, time, and frequency domains can be captured, and the aforementioned UAV channel-related parameters have a great impact on channel statistics. Finally, the utility of the proposed model is validated by the excellent agreement between simulation results and measurements.</description><identifier>ISSN: 0018-926X</identifier><identifier>EISSN: 1558-2221</identifier><identifier>DOI: 10.1109/TAP.2022.3209192</identifier><identifier>CODEN: IETPAK</identifier><language>eng</language><publisher>IEEE</publisher><subject>6G mobile communication ; Autonomous aerial vehicles ; Channel models ; Massive MIMO ; Millimeter wave communication ; mmWave ; space-time-frequency non-stationarity ; Stochastic processes ; UAV channels ; Wideband</subject><ispartof>IEEE transactions on antennas and propagation, 2022, p.1-1</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0001-5705-0912 ; 0000-0002-5943-0326</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9906829$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,4024,27923,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9906829$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Xu, Jinpeng</creatorcontrib><creatorcontrib>Cheng, Xiang</creatorcontrib><creatorcontrib>Bai, Lin</creatorcontrib><title>A 3D Space-Time-Frequency Non-Stationary Model for Low-Altitude UAV mmWave and Massive MIMO Aerial Fading Channels</title><title>IEEE transactions on antennas and propagation</title><addtitle>TAP</addtitle><description>In this paper, a three-dimensional (3D) geometry-based stochastic model (GBSM) is proposed for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) unmanned aerial vehicle (UAV) channels. The proposed model is the first mmWave massive MIMO UAV two-cylinder GBSM that enables to jointly model the channel space-time-frequency non-stationarity by a novel variable parameter-based method. In this novel method, key parameters of UAV channels are assumed to vary in space, time, and frequency domains, and the effect of the unique UAV-related parameters is further taken into account, such as the UAV's altitude, velocity, and moving directions. Based on the proposed model, some statistical properties are derived, including the time-variant transfer function (TVTF), space-time-frequency correlation function (STF-CF), Doppler power spectral density (PSD), and the standard deviation of Doppler frequency on antenna arrays. Simulation results show that the channel non-stationarity in space, time, and frequency domains can be captured, and the aforementioned UAV channel-related parameters have a great impact on channel statistics. Finally, the utility of the proposed model is validated by the excellent agreement between simulation results and measurements.</description><subject>6G mobile communication</subject><subject>Autonomous aerial vehicles</subject><subject>Channel models</subject><subject>Massive MIMO</subject><subject>Millimeter wave communication</subject><subject>mmWave</subject><subject>space-time-frequency non-stationarity</subject><subject>Stochastic processes</subject><subject>UAV channels</subject><subject>Wideband</subject><issn>0018-926X</issn><issn>1558-2221</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9jctOwzAQRS0EEuGxR2IzP-BgT9IqXlqFCCQCSA2PXTVqpmCU2MVOQf17smDN6p6js7hCXGiVa63MVWufclSIeYHKaIMHItOzWSURUR-KTCldSYPzt2NxktLnpGVVlpmIFoprWG5pzbJ1A8s68teO_XoPD8HL5UijC57iHprQcQ-bEOE-_Ejbj27cdQzP9gWG4ZW-Gch30FBKbuLmrnkEy9FRDzV1zr_D4oO85z6diaMN9YnP__ZUXNY37eJWOmZebaMbpruVMWpeoSn-r79VO0mX</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Xu, Jinpeng</creator><creator>Cheng, Xiang</creator><creator>Bai, Lin</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><orcidid>https://orcid.org/0000-0001-5705-0912</orcidid><orcidid>https://orcid.org/0000-0002-5943-0326</orcidid></search><sort><creationdate>2022</creationdate><title>A 3D Space-Time-Frequency Non-Stationary Model for Low-Altitude UAV mmWave and Massive MIMO Aerial Fading Channels</title><author>Xu, Jinpeng ; Cheng, Xiang ; Bai, Lin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_99068293</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>6G mobile communication</topic><topic>Autonomous aerial vehicles</topic><topic>Channel models</topic><topic>Massive MIMO</topic><topic>Millimeter wave communication</topic><topic>mmWave</topic><topic>space-time-frequency non-stationarity</topic><topic>Stochastic processes</topic><topic>UAV channels</topic><topic>Wideband</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xu, Jinpeng</creatorcontrib><creatorcontrib>Cheng, Xiang</creatorcontrib><creatorcontrib>Bai, Lin</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005–Present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998–Present</collection><collection>IEEE Xplore</collection><jtitle>IEEE transactions on antennas and propagation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Xu, Jinpeng</au><au>Cheng, Xiang</au><au>Bai, Lin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A 3D Space-Time-Frequency Non-Stationary Model for Low-Altitude UAV mmWave and Massive MIMO Aerial Fading Channels</atitle><jtitle>IEEE transactions on antennas and propagation</jtitle><stitle>TAP</stitle><date>2022</date><risdate>2022</risdate><spage>1</spage><epage>1</epage><pages>1-1</pages><issn>0018-926X</issn><eissn>1558-2221</eissn><coden>IETPAK</coden><abstract>In this paper, a three-dimensional (3D) geometry-based stochastic model (GBSM) is proposed for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) unmanned aerial vehicle (UAV) channels. The proposed model is the first mmWave massive MIMO UAV two-cylinder GBSM that enables to jointly model the channel space-time-frequency non-stationarity by a novel variable parameter-based method. In this novel method, key parameters of UAV channels are assumed to vary in space, time, and frequency domains, and the effect of the unique UAV-related parameters is further taken into account, such as the UAV's altitude, velocity, and moving directions. Based on the proposed model, some statistical properties are derived, including the time-variant transfer function (TVTF), space-time-frequency correlation function (STF-CF), Doppler power spectral density (PSD), and the standard deviation of Doppler frequency on antenna arrays. Simulation results show that the channel non-stationarity in space, time, and frequency domains can be captured, and the aforementioned UAV channel-related parameters have a great impact on channel statistics. 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subjects | 6G mobile communication Autonomous aerial vehicles Channel models Massive MIMO Millimeter wave communication mmWave space-time-frequency non-stationarity Stochastic processes UAV channels Wideband |
title | A 3D Space-Time-Frequency Non-Stationary Model for Low-Altitude UAV mmWave and Massive MIMO Aerial Fading Channels |
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