Rate Optimization for Multiuser MIMO Systems With Linear Processing

This paper focuses on linear transceiver design for rate optimization in multiuser Gaussian multple-input multiple-output (MIMO) systems. We focus on two design criteria: 1) maximizing the weighted sum rate subject to a total power constraint; 2) maximizing the minimum user rate subject to a total p...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on signal processing 2008-08, Vol.56 (8), p.4020-4030
Hauptverfasser: Shuying Shi, Schubert, M., Boche, H.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 4030
container_issue 8
container_start_page 4020
container_title IEEE transactions on signal processing
container_volume 56
creator Shuying Shi
Schubert, M.
Boche, H.
description This paper focuses on linear transceiver design for rate optimization in multiuser Gaussian multple-input multiple-output (MIMO) systems. We focus on two design criteria: 1) maximizing the weighted sum rate subject to a total power constraint; 2) maximizing the minimum user rate subject to a total power constraint. For these problems, new power allocation strategies are derived, which can be formulated as geometric programs (GPs) involving mean square errors (MSEs). Based on these solutions, we propose iterative algorithms, where each iteration contains the optimization of the uplink power, uplink receive filters, and downlink receive filters. Monotonic convergence of the algorithms is proved. Simulations show that the algorithms outperform existing linear schemes. Additionally, we extend the results to other variations of the problems, such as, the problem of sum-rate constrained or user-rate constrained power minimization, and the problem of sum-rate maximization subject to user-rate constraints and a total power constraint.
doi_str_mv 10.1109/TSP.2008.924796
format Article
fullrecord <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_journals_863203829</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>4509444</ieee_id><sourcerecordid>880646595</sourcerecordid><originalsourceid>FETCH-LOGICAL-c350t-2347bfc2e12174e1fa232c36301f3a730f312bf7586a8fd3847bd9d7134679f3</originalsourceid><addsrcrecordid>eNpdkEtLAzEURgdRsFbXLtwMgriaNu_HUoqPQkuLLegupGmiKdOZmsws6q83ZUoXru4H93yXy8myWwgGEAI5XC7mAwSAGEhEuGRnWQ9KAgtAODtPGVBcUME_L7OrGDcAQEIk62Wjd93YfLZr_Nb_6sbXVe7qkE_bsvFttCmNp7N8sY-N3cb8wzff-cRXVod8HmpjY_TV13V24XQZ7c1x9rPly_Ny9FZMZq_j0dOkMJiCpkCY8JUzyEIEObHQaYSRwQwD6LDmGDgM0cpxKpgWbo1FwtdyzSEmjEuH-9ljd3YX6p_WxkZtfTS2LHVl6zYqIQAjjEqayPt_5KZuQ5V-U4JhBLBAMkHDDjKhjjFYp3bBb3XYKwjUwahKRtXBqOqMpsbD8ayORpcu6Mr4eKohQBGDGCburuO8tfa0JhRIQgj-A9hufNY</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>863203829</pqid></control><display><type>article</type><title>Rate Optimization for Multiuser MIMO Systems With Linear Processing</title><source>IEEE Electronic Library (IEL)</source><creator>Shuying Shi ; Schubert, M. ; Boche, H.</creator><creatorcontrib>Shuying Shi ; Schubert, M. ; Boche, H.</creatorcontrib><description>This paper focuses on linear transceiver design for rate optimization in multiuser Gaussian multple-input multiple-output (MIMO) systems. We focus on two design criteria: 1) maximizing the weighted sum rate subject to a total power constraint; 2) maximizing the minimum user rate subject to a total power constraint. For these problems, new power allocation strategies are derived, which can be formulated as geometric programs (GPs) involving mean square errors (MSEs). Based on these solutions, we propose iterative algorithms, where each iteration contains the optimization of the uplink power, uplink receive filters, and downlink receive filters. Monotonic convergence of the algorithms is proved. Simulations show that the algorithms outperform existing linear schemes. Additionally, we extend the results to other variations of the problems, such as, the problem of sum-rate constrained or user-rate constrained power minimization, and the problem of sum-rate maximization subject to user-rate constraints and a total power constraint.</description><identifier>ISSN: 1053-587X</identifier><identifier>EISSN: 1941-0476</identifier><identifier>DOI: 10.1109/TSP.2008.924796</identifier><identifier>CODEN: ITPRED</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Algorithms ; Allocations ; Applied sciences ; Broadcasting ; Constraints ; Convergence ; Design engineering ; Design optimization ; Downlink ; Exact sciences and technology ; Filters ; Information, signal and communications theory ; Iterative algorithms ; Laboratories ; Max-min fairness ; Maximization ; Mean square error methods ; MIMO ; Miscellaneous ; Mobile communication ; multiuser multiinput-multioutput (MIMO) ; Optimization ; Signal processing ; Strategy ; sum-rate optimization ; Telecommunications and information theory ; transceiver design ; Transceivers</subject><ispartof>IEEE transactions on signal processing, 2008-08, Vol.56 (8), p.4020-4030</ispartof><rights>2008 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2008</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c350t-2347bfc2e12174e1fa232c36301f3a730f312bf7586a8fd3847bd9d7134679f3</citedby><cites>FETCH-LOGICAL-c350t-2347bfc2e12174e1fa232c36301f3a730f312bf7586a8fd3847bd9d7134679f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4509444$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,782,786,798,27931,27932,54765</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4509444$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&amp;idt=20526131$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Shuying Shi</creatorcontrib><creatorcontrib>Schubert, M.</creatorcontrib><creatorcontrib>Boche, H.</creatorcontrib><title>Rate Optimization for Multiuser MIMO Systems With Linear Processing</title><title>IEEE transactions on signal processing</title><addtitle>TSP</addtitle><description>This paper focuses on linear transceiver design for rate optimization in multiuser Gaussian multple-input multiple-output (MIMO) systems. We focus on two design criteria: 1) maximizing the weighted sum rate subject to a total power constraint; 2) maximizing the minimum user rate subject to a total power constraint. For these problems, new power allocation strategies are derived, which can be formulated as geometric programs (GPs) involving mean square errors (MSEs). Based on these solutions, we propose iterative algorithms, where each iteration contains the optimization of the uplink power, uplink receive filters, and downlink receive filters. Monotonic convergence of the algorithms is proved. Simulations show that the algorithms outperform existing linear schemes. Additionally, we extend the results to other variations of the problems, such as, the problem of sum-rate constrained or user-rate constrained power minimization, and the problem of sum-rate maximization subject to user-rate constraints and a total power constraint.</description><subject>Algorithms</subject><subject>Allocations</subject><subject>Applied sciences</subject><subject>Broadcasting</subject><subject>Constraints</subject><subject>Convergence</subject><subject>Design engineering</subject><subject>Design optimization</subject><subject>Downlink</subject><subject>Exact sciences and technology</subject><subject>Filters</subject><subject>Information, signal and communications theory</subject><subject>Iterative algorithms</subject><subject>Laboratories</subject><subject>Max-min fairness</subject><subject>Maximization</subject><subject>Mean square error methods</subject><subject>MIMO</subject><subject>Miscellaneous</subject><subject>Mobile communication</subject><subject>multiuser multiinput-multioutput (MIMO)</subject><subject>Optimization</subject><subject>Signal processing</subject><subject>Strategy</subject><subject>sum-rate optimization</subject><subject>Telecommunications and information theory</subject><subject>transceiver design</subject><subject>Transceivers</subject><issn>1053-587X</issn><issn>1941-0476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkEtLAzEURgdRsFbXLtwMgriaNu_HUoqPQkuLLegupGmiKdOZmsws6q83ZUoXru4H93yXy8myWwgGEAI5XC7mAwSAGEhEuGRnWQ9KAgtAODtPGVBcUME_L7OrGDcAQEIk62Wjd93YfLZr_Nb_6sbXVe7qkE_bsvFttCmNp7N8sY-N3cb8wzff-cRXVod8HmpjY_TV13V24XQZ7c1x9rPly_Ny9FZMZq_j0dOkMJiCpkCY8JUzyEIEObHQaYSRwQwD6LDmGDgM0cpxKpgWbo1FwtdyzSEmjEuH-9ljd3YX6p_WxkZtfTS2LHVl6zYqIQAjjEqayPt_5KZuQ5V-U4JhBLBAMkHDDjKhjjFYp3bBb3XYKwjUwahKRtXBqOqMpsbD8ayORpcu6Mr4eKohQBGDGCburuO8tfa0JhRIQgj-A9hufNY</recordid><startdate>20080801</startdate><enddate>20080801</enddate><creator>Shuying Shi</creator><creator>Schubert, M.</creator><creator>Boche, H.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</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>20080801</creationdate><title>Rate Optimization for Multiuser MIMO Systems With Linear Processing</title><author>Shuying Shi ; Schubert, M. ; Boche, H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c350t-2347bfc2e12174e1fa232c36301f3a730f312bf7586a8fd3847bd9d7134679f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Algorithms</topic><topic>Allocations</topic><topic>Applied sciences</topic><topic>Broadcasting</topic><topic>Constraints</topic><topic>Convergence</topic><topic>Design engineering</topic><topic>Design optimization</topic><topic>Downlink</topic><topic>Exact sciences and technology</topic><topic>Filters</topic><topic>Information, signal and communications theory</topic><topic>Iterative algorithms</topic><topic>Laboratories</topic><topic>Max-min fairness</topic><topic>Maximization</topic><topic>Mean square error methods</topic><topic>MIMO</topic><topic>Miscellaneous</topic><topic>Mobile communication</topic><topic>multiuser multiinput-multioutput (MIMO)</topic><topic>Optimization</topic><topic>Signal processing</topic><topic>Strategy</topic><topic>sum-rate optimization</topic><topic>Telecommunications and information theory</topic><topic>transceiver design</topic><topic>Transceivers</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shuying Shi</creatorcontrib><creatorcontrib>Schubert, M.</creatorcontrib><creatorcontrib>Boche, H.</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>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics &amp; 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 &amp; 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>Shuying Shi</au><au>Schubert, M.</au><au>Boche, H.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Rate Optimization for Multiuser MIMO Systems With Linear Processing</atitle><jtitle>IEEE transactions on signal processing</jtitle><stitle>TSP</stitle><date>2008-08-01</date><risdate>2008</risdate><volume>56</volume><issue>8</issue><spage>4020</spage><epage>4030</epage><pages>4020-4030</pages><issn>1053-587X</issn><eissn>1941-0476</eissn><coden>ITPRED</coden><abstract>This paper focuses on linear transceiver design for rate optimization in multiuser Gaussian multple-input multiple-output (MIMO) systems. We focus on two design criteria: 1) maximizing the weighted sum rate subject to a total power constraint; 2) maximizing the minimum user rate subject to a total power constraint. For these problems, new power allocation strategies are derived, which can be formulated as geometric programs (GPs) involving mean square errors (MSEs). Based on these solutions, we propose iterative algorithms, where each iteration contains the optimization of the uplink power, uplink receive filters, and downlink receive filters. Monotonic convergence of the algorithms is proved. Simulations show that the algorithms outperform existing linear schemes. Additionally, we extend the results to other variations of the problems, such as, the problem of sum-rate constrained or user-rate constrained power minimization, and the problem of sum-rate maximization subject to user-rate constraints and a total power constraint.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TSP.2008.924796</doi><tpages>11</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 1053-587X
ispartof IEEE transactions on signal processing, 2008-08, Vol.56 (8), p.4020-4030
issn 1053-587X
1941-0476
language eng
recordid cdi_proquest_journals_863203829
source IEEE Electronic Library (IEL)
subjects Algorithms
Allocations
Applied sciences
Broadcasting
Constraints
Convergence
Design engineering
Design optimization
Downlink
Exact sciences and technology
Filters
Information, signal and communications theory
Iterative algorithms
Laboratories
Max-min fairness
Maximization
Mean square error methods
MIMO
Miscellaneous
Mobile communication
multiuser multiinput-multioutput (MIMO)
Optimization
Signal processing
Strategy
sum-rate optimization
Telecommunications and information theory
transceiver design
Transceivers
title Rate Optimization for Multiuser MIMO Systems With Linear Processing
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-05T09%3A09%3A15IST&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=Rate%20Optimization%20for%20Multiuser%20MIMO%20Systems%20With%20Linear%20Processing&rft.jtitle=IEEE%20transactions%20on%20signal%20processing&rft.au=Shuying%20Shi&rft.date=2008-08-01&rft.volume=56&rft.issue=8&rft.spage=4020&rft.epage=4030&rft.pages=4020-4030&rft.issn=1053-587X&rft.eissn=1941-0476&rft.coden=ITPRED&rft_id=info:doi/10.1109/TSP.2008.924796&rft_dat=%3Cproquest_RIE%3E880646595%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=863203829&rft_id=info:pmid/&rft_ieee_id=4509444&rfr_iscdi=true