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...
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Veröffentlicht in: | IEEE transactions on signal processing 2008-08, Vol.56 (8), p.4020-4030 |
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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 |
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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. 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(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>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=20526131$$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 & 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>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> |
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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 |
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