Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO
This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi-output (MIMO) systems, which adapts training overhead and pilot design to reliably estimate and feed back the downlink channel state inf...
Gespeichert in:
Veröffentlicht in: | IEEE transactions on signal processing 2015-12, Vol.63 (23), p.6169-6183 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 6183 |
---|---|
container_issue | 23 |
container_start_page | 6169 |
container_title | IEEE transactions on signal processing |
container_volume | 63 |
creator | Gao, Zhen Dai, Linglong Wang, Zhaocheng Chen, Sheng |
description | This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi-output (MIMO) systems, which adapts training overhead and pilot design to reliably estimate and feed back the downlink channel state information (CSI) with significantly reduced overhead. Specifically, a nonorthogonal downlink pilot design is first proposed, which is very different from standard orthogonal pilots. By exploiting the spatially common sparsity of massive MIMO channels, a compressive sensing (CS) based adaptive CSI acquisition scheme is proposed, where the consumed time slot overhead only adaptively depends on the sparsity level of the channels. In addition, a distributed sparsity adaptive matching pursuit algorithm is proposed to jointly estimate the channels of multiple subcarriers. Furthermore, by exploiting the temporal channel correlation, a closed-loop channel tracking scheme is provided, which adaptively designs the nonorthogonal pilot according to the previous channel estimation to achieve an enhanced CSI acquisition. Finally, we generalize the results of the multiple-measurement-vectors case in CS and derive the Cramér-Rao lower bound of the proposed scheme, which enlightens us to design the nonorthogonal pilot signals for the improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterparts, and it is capable of approaching the performance bound. |
doi_str_mv | 10.1109/TSP.2015.2463260 |
format | Article |
fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_miscellaneous_1778037595</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>7174558</ieee_id><sourcerecordid>1778037595</sourcerecordid><originalsourceid>FETCH-LOGICAL-c437t-281f57d387e6917103a1331b6f84d737a02c1c4d995a013cba8efda07b84a53</originalsourceid><addsrcrecordid>eNpdkM9LwzAUx4soOKd3wUvAi5fOvCZp0uPsNhU2JmwHLxLSJsXO_jLphP33pmx48PQej8_38d4nCG4BTwBw8rjdvE0iDGwS0ZhEMT4LRpBQCDHl8bnvMSMhE_z9MrhybocxUJrEo-Bj06m-VFV1QGlb122D_MC6sj-gJ-WMRlOtur78MSj9VE1jKjR3fVn7jEdVo9HCGJ2p_AsVrUWL2QytlHMDv3pdra-Di0JVztyc6jjYLObb9CVcrp9f0-kyzCnhfRgJKBjXRHATJ8ABEwWEQBYXgmpOuMJRDjnVScIUBpJnSphCK8wzQRUj4-DhuLWz7ffeuF7WpctNVanGtHsngXOBCWfJgN7_Q3ft3jb-Nk9FCaUYBHgKH6ncts5ZU8jO-p_tQQKWg23pbcvBtjzZ9pG7Y6Q0xvzhHDhlTJBfOch5Og</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1729440181</pqid></control><display><type>article</type><title>Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO</title><source>IEEE Electronic Library (IEL)</source><creator>Gao, Zhen ; Dai, Linglong ; Wang, Zhaocheng ; Chen, Sheng</creator><creatorcontrib>Gao, Zhen ; Dai, Linglong ; Wang, Zhaocheng ; Chen, Sheng</creatorcontrib><description>This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi-output (MIMO) systems, which adapts training overhead and pilot design to reliably estimate and feed back the downlink channel state information (CSI) with significantly reduced overhead. Specifically, a nonorthogonal downlink pilot design is first proposed, which is very different from standard orthogonal pilots. By exploiting the spatially common sparsity of massive MIMO channels, a compressive sensing (CS) based adaptive CSI acquisition scheme is proposed, where the consumed time slot overhead only adaptively depends on the sparsity level of the channels. In addition, a distributed sparsity adaptive matching pursuit algorithm is proposed to jointly estimate the channels of multiple subcarriers. Furthermore, by exploiting the temporal channel correlation, a closed-loop channel tracking scheme is provided, which adaptively designs the nonorthogonal pilot according to the previous channel estimation to achieve an enhanced CSI acquisition. Finally, we generalize the results of the multiple-measurement-vectors case in CS and derive the Cramér-Rao lower bound of the proposed scheme, which enlightens us to design the nonorthogonal pilot signals for the improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterparts, and it is capable of approaching the performance bound.</description><identifier>ISSN: 1053-587X</identifier><identifier>EISSN: 1941-0476</identifier><identifier>DOI: 10.1109/TSP.2015.2463260</identifier><identifier>CODEN: ITPRED</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Acquisitions ; Algorithm design and analysis ; Algorithms ; Channel estimation ; Channels ; compressive sensing ; Design engineering ; Downlink ; Estimates ; Feedback ; frequency division duplex ; Marketing ; massive multi-input multi-output ; Matching pursuit algorithms ; MIMO ; Pilots ; Signal processing algorithms ; spatially common sparsity ; temporal correlation ; Training</subject><ispartof>IEEE transactions on signal processing, 2015-12, Vol.63 (23), p.6169-6183</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Dec 2015</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c437t-281f57d387e6917103a1331b6f84d737a02c1c4d995a013cba8efda07b84a53</citedby><cites>FETCH-LOGICAL-c437t-281f57d387e6917103a1331b6f84d737a02c1c4d995a013cba8efda07b84a53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7174558$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27903,27904,54736</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7174558$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Gao, Zhen</creatorcontrib><creatorcontrib>Dai, Linglong</creatorcontrib><creatorcontrib>Wang, Zhaocheng</creatorcontrib><creatorcontrib>Chen, Sheng</creatorcontrib><title>Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO</title><title>IEEE transactions on signal processing</title><addtitle>TSP</addtitle><description>This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi-output (MIMO) systems, which adapts training overhead and pilot design to reliably estimate and feed back the downlink channel state information (CSI) with significantly reduced overhead. Specifically, a nonorthogonal downlink pilot design is first proposed, which is very different from standard orthogonal pilots. By exploiting the spatially common sparsity of massive MIMO channels, a compressive sensing (CS) based adaptive CSI acquisition scheme is proposed, where the consumed time slot overhead only adaptively depends on the sparsity level of the channels. In addition, a distributed sparsity adaptive matching pursuit algorithm is proposed to jointly estimate the channels of multiple subcarriers. Furthermore, by exploiting the temporal channel correlation, a closed-loop channel tracking scheme is provided, which adaptively designs the nonorthogonal pilot according to the previous channel estimation to achieve an enhanced CSI acquisition. Finally, we generalize the results of the multiple-measurement-vectors case in CS and derive the Cramér-Rao lower bound of the proposed scheme, which enlightens us to design the nonorthogonal pilot signals for the improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterparts, and it is capable of approaching the performance bound.</description><subject>Acquisitions</subject><subject>Algorithm design and analysis</subject><subject>Algorithms</subject><subject>Channel estimation</subject><subject>Channels</subject><subject>compressive sensing</subject><subject>Design engineering</subject><subject>Downlink</subject><subject>Estimates</subject><subject>Feedback</subject><subject>frequency division duplex</subject><subject>Marketing</subject><subject>massive multi-input multi-output</subject><subject>Matching pursuit algorithms</subject><subject>MIMO</subject><subject>Pilots</subject><subject>Signal processing algorithms</subject><subject>spatially common sparsity</subject><subject>temporal correlation</subject><subject>Training</subject><issn>1053-587X</issn><issn>1941-0476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkM9LwzAUx4soOKd3wUvAi5fOvCZp0uPsNhU2JmwHLxLSJsXO_jLphP33pmx48PQej8_38d4nCG4BTwBw8rjdvE0iDGwS0ZhEMT4LRpBQCDHl8bnvMSMhE_z9MrhybocxUJrEo-Bj06m-VFV1QGlb122D_MC6sj-gJ-WMRlOtur78MSj9VE1jKjR3fVn7jEdVo9HCGJ2p_AsVrUWL2QytlHMDv3pdra-Di0JVztyc6jjYLObb9CVcrp9f0-kyzCnhfRgJKBjXRHATJ8ABEwWEQBYXgmpOuMJRDjnVScIUBpJnSphCK8wzQRUj4-DhuLWz7ffeuF7WpctNVanGtHsngXOBCWfJgN7_Q3ft3jb-Nk9FCaUYBHgKH6ncts5ZU8jO-p_tQQKWg23pbcvBtjzZ9pG7Y6Q0xvzhHDhlTJBfOch5Og</recordid><startdate>20151201</startdate><enddate>20151201</enddate><creator>Gao, Zhen</creator><creator>Dai, Linglong</creator><creator>Wang, Zhaocheng</creator><creator>Chen, Sheng</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>20151201</creationdate><title>Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO</title><author>Gao, Zhen ; Dai, Linglong ; Wang, Zhaocheng ; Chen, Sheng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c437t-281f57d387e6917103a1331b6f84d737a02c1c4d995a013cba8efda07b84a53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Acquisitions</topic><topic>Algorithm design and analysis</topic><topic>Algorithms</topic><topic>Channel estimation</topic><topic>Channels</topic><topic>compressive sensing</topic><topic>Design engineering</topic><topic>Downlink</topic><topic>Estimates</topic><topic>Feedback</topic><topic>frequency division duplex</topic><topic>Marketing</topic><topic>massive multi-input multi-output</topic><topic>Matching pursuit algorithms</topic><topic>MIMO</topic><topic>Pilots</topic><topic>Signal processing algorithms</topic><topic>spatially common sparsity</topic><topic>temporal correlation</topic><topic>Training</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gao, Zhen</creatorcontrib><creatorcontrib>Dai, Linglong</creatorcontrib><creatorcontrib>Wang, Zhaocheng</creatorcontrib><creatorcontrib>Chen, Sheng</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on signal processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Gao, Zhen</au><au>Dai, Linglong</au><au>Wang, Zhaocheng</au><au>Chen, Sheng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO</atitle><jtitle>IEEE transactions on signal processing</jtitle><stitle>TSP</stitle><date>2015-12-01</date><risdate>2015</risdate><volume>63</volume><issue>23</issue><spage>6169</spage><epage>6183</epage><pages>6169-6183</pages><issn>1053-587X</issn><eissn>1941-0476</eissn><coden>ITPRED</coden><abstract>This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi-output (MIMO) systems, which adapts training overhead and pilot design to reliably estimate and feed back the downlink channel state information (CSI) with significantly reduced overhead. Specifically, a nonorthogonal downlink pilot design is first proposed, which is very different from standard orthogonal pilots. By exploiting the spatially common sparsity of massive MIMO channels, a compressive sensing (CS) based adaptive CSI acquisition scheme is proposed, where the consumed time slot overhead only adaptively depends on the sparsity level of the channels. In addition, a distributed sparsity adaptive matching pursuit algorithm is proposed to jointly estimate the channels of multiple subcarriers. Furthermore, by exploiting the temporal channel correlation, a closed-loop channel tracking scheme is provided, which adaptively designs the nonorthogonal pilot according to the previous channel estimation to achieve an enhanced CSI acquisition. Finally, we generalize the results of the multiple-measurement-vectors case in CS and derive the Cramér-Rao lower bound of the proposed scheme, which enlightens us to design the nonorthogonal pilot signals for the improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterparts, and it is capable of approaching the performance bound.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TSP.2015.2463260</doi><tpages>15</tpages></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | ISSN: 1053-587X |
ispartof | IEEE transactions on signal processing, 2015-12, Vol.63 (23), p.6169-6183 |
issn | 1053-587X 1941-0476 |
language | eng |
recordid | cdi_proquest_miscellaneous_1778037595 |
source | IEEE Electronic Library (IEL) |
subjects | Acquisitions Algorithm design and analysis Algorithms Channel estimation Channels compressive sensing Design engineering Downlink Estimates Feedback frequency division duplex Marketing massive multi-input multi-output Matching pursuit algorithms MIMO Pilots Signal processing algorithms spatially common sparsity temporal correlation Training |
title | Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-24T00%3A33%3A02IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Spatially%20Common%20Sparsity%20Based%20Adaptive%20Channel%20Estimation%20and%20Feedback%20for%20FDD%20Massive%20MIMO&rft.jtitle=IEEE%20transactions%20on%20signal%20processing&rft.au=Gao,%20Zhen&rft.date=2015-12-01&rft.volume=63&rft.issue=23&rft.spage=6169&rft.epage=6183&rft.pages=6169-6183&rft.issn=1053-587X&rft.eissn=1941-0476&rft.coden=ITPRED&rft_id=info:doi/10.1109/TSP.2015.2463260&rft_dat=%3Cproquest_RIE%3E1778037595%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1729440181&rft_id=info:pmid/&rft_ieee_id=7174558&rfr_iscdi=true |