Low-Complexity GSVD-Based Beamforming and Power Allocation for a Cognitive Radio Network
In this paper, low-complexity generalized singular value decomposition (GSVD) based beamforming schemes are proposed for a cognitive radio (CR) network in which multiple secondary users (SUs) with multiple antennas coexist with multiple primary users (PUs). In general, optimal beamforming, which sup...
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| Veröffentlicht in: | IEICE Transactions on Communications 2012/11/01, Vol.E95.B(11), pp.3536-3544 |
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| container_title | IEICE Transactions on Communications |
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| creator | PARK, Jaehyun PARK, Yunju HWANG, Sunghyun JEONG, Byung Jang |
| description | In this paper, low-complexity generalized singular value decomposition (GSVD) based beamforming schemes are proposed for a cognitive radio (CR) network in which multiple secondary users (SUs) with multiple antennas coexist with multiple primary users (PUs). In general, optimal beamforming, which suppresses the interference caused at PUs to below a certain threshold and maximizes the signal-to-interference-plus-noise ratios (SINRs) of multiple SUs simultaneously, requires a complicated iterative optimization process. To overcome the computational complexity, we introduce a signal-to-leakage-plus-noise ratio (SLNR) maximizing beamforming scheme in which the weight can be obtained by using the GSVD algorithm, and does not require any iterations or matrix squaring operations. Here, to satisfy the leakage constraints at PUs, two linear methods, zero forcing (ZF) preprocessing and power allocation, are proposed. |
| doi_str_mv | 10.1587/transcom.E95.B.3536 |
| format | Article |
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In general, optimal beamforming, which suppresses the interference caused at PUs to below a certain threshold and maximizes the signal-to-interference-plus-noise ratios (SINRs) of multiple SUs simultaneously, requires a complicated iterative optimization process. To overcome the computational complexity, we introduce a signal-to-leakage-plus-noise ratio (SLNR) maximizing beamforming scheme in which the weight can be obtained by using the GSVD algorithm, and does not require any iterations or matrix squaring operations. Here, to satisfy the leakage constraints at PUs, two linear methods, zero forcing (ZF) preprocessing and power allocation, are proposed.</description><identifier>ISSN: 0916-8516</identifier><identifier>EISSN: 1745-1345</identifier><identifier>DOI: 10.1587/transcom.E95.B.3536</identifier><language>eng</language><publisher>Tokyo: The Institute of Electronics, Information and Communication Engineers</publisher><subject>Allocations ; Antennas ; Applied sciences ; Beamforming ; Cognitive radio ; cognitive radio network ; Detection, estimation, filtering, equalization, prediction ; Exact sciences and technology ; GSVD algorithm ; Information, signal and communications theory ; Iterative methods ; multi-user MIMO ; Networks ; Optimization ; power allocation ; Preprocessing ; Radiocommunication specific techniques ; Radiocommunications ; Signal and communications theory ; Signal, noise ; Systems, networks and services of telecommunications ; Telecommunications ; Telecommunications and information theory ; Transmission and modulation (techniques and equipments)</subject><ispartof>IEICE Transactions on Communications, 2012/11/01, Vol.E95.B(11), pp.3536-3544</ispartof><rights>2012 The Institute of Electronics, Information and Communication Engineers</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c502t-22938a6f3ae9d1625e22c6b82700ccf4cb11931abcfa63f1ec3a8f82ea8699653</citedby><cites>FETCH-LOGICAL-c502t-22938a6f3ae9d1625e22c6b82700ccf4cb11931abcfa63f1ec3a8f82ea8699653</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,4024,27923,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=26619508$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>PARK, Jaehyun</creatorcontrib><creatorcontrib>PARK, Yunju</creatorcontrib><creatorcontrib>HWANG, Sunghyun</creatorcontrib><creatorcontrib>JEONG, Byung Jang</creatorcontrib><title>Low-Complexity GSVD-Based Beamforming and Power Allocation for a Cognitive Radio Network</title><title>IEICE Transactions on Communications</title><addtitle>IEICE Trans. Commun.</addtitle><description>In this paper, low-complexity generalized singular value decomposition (GSVD) based beamforming schemes are proposed for a cognitive radio (CR) network in which multiple secondary users (SUs) with multiple antennas coexist with multiple primary users (PUs). In general, optimal beamforming, which suppresses the interference caused at PUs to below a certain threshold and maximizes the signal-to-interference-plus-noise ratios (SINRs) of multiple SUs simultaneously, requires a complicated iterative optimization process. To overcome the computational complexity, we introduce a signal-to-leakage-plus-noise ratio (SLNR) maximizing beamforming scheme in which the weight can be obtained by using the GSVD algorithm, and does not require any iterations or matrix squaring operations. Here, to satisfy the leakage constraints at PUs, two linear methods, zero forcing (ZF) preprocessing and power allocation, are proposed.</description><subject>Allocations</subject><subject>Antennas</subject><subject>Applied sciences</subject><subject>Beamforming</subject><subject>Cognitive radio</subject><subject>cognitive radio network</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Exact sciences and technology</subject><subject>GSVD algorithm</subject><subject>Information, signal and communications theory</subject><subject>Iterative methods</subject><subject>multi-user MIMO</subject><subject>Networks</subject><subject>Optimization</subject><subject>power allocation</subject><subject>Preprocessing</subject><subject>Radiocommunication specific techniques</subject><subject>Radiocommunications</subject><subject>Signal and communications theory</subject><subject>Signal, noise</subject><subject>Systems, networks and services of telecommunications</subject><subject>Telecommunications</subject><subject>Telecommunications and information theory</subject><subject>Transmission and modulation (techniques and equipments)</subject><issn>0916-8516</issn><issn>1745-1345</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><recordid>eNpdkE1vEzEURUcIJELhF7DxBonNBD87duxlE0oBRbTiS-ysF-c5uMyMg-2S9t8zVUqEunqLd-690mmal8CnoMz8Tc04FJ_66ZlV08VUKqkfNROYz1QLcqYeNxNuQbdGgX7aPCvlinMwAsSk-bFK-3aZ-l1HN7HesvMv39-2Cyy0YQvCPqTcx2HLcNiwy7SnzE67LnmsMQ1sfDJky7QdYo1_iH3GTUzsE9V9yr-eN08CdoVe3N-T5tu7s6_L9-3q4vzD8nTVesVFbYWw0qAOEsluQAtFQni9NmLOufdh5tcAVgKufUAtA5CXaIIRhEZbq5U8aV4fenc5_b6mUl0fi6euw4HSdXEgQWltZ0KMqDygPqdSMgW3y7HHfOuAuzuP7p9HN3p0C3fncUy9uh_A4rELI-JjOUaF1mAVNyP38cBdlYpbOgKYa_QdPewG-G_kCPmfmB0N8i-xh5Ct</recordid><startdate>2012</startdate><enddate>2012</enddate><creator>PARK, Jaehyun</creator><creator>PARK, Yunju</creator><creator>HWANG, Sunghyun</creator><creator>JEONG, Byung Jang</creator><general>The Institute of Electronics, Information and Communication Engineers</general><general>Communications Society</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope></search><sort><creationdate>2012</creationdate><title>Low-Complexity GSVD-Based Beamforming and Power Allocation for a Cognitive Radio Network</title><author>PARK, Jaehyun ; PARK, Yunju ; HWANG, Sunghyun ; JEONG, Byung Jang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c502t-22938a6f3ae9d1625e22c6b82700ccf4cb11931abcfa63f1ec3a8f82ea8699653</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Allocations</topic><topic>Antennas</topic><topic>Applied sciences</topic><topic>Beamforming</topic><topic>Cognitive radio</topic><topic>cognitive radio network</topic><topic>Detection, estimation, filtering, equalization, prediction</topic><topic>Exact sciences and technology</topic><topic>GSVD algorithm</topic><topic>Information, signal and communications theory</topic><topic>Iterative methods</topic><topic>multi-user MIMO</topic><topic>Networks</topic><topic>Optimization</topic><topic>power allocation</topic><topic>Preprocessing</topic><topic>Radiocommunication specific techniques</topic><topic>Radiocommunications</topic><topic>Signal and communications theory</topic><topic>Signal, noise</topic><topic>Systems, networks and services of telecommunications</topic><topic>Telecommunications</topic><topic>Telecommunications and information theory</topic><topic>Transmission and modulation (techniques and equipments)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>PARK, Jaehyun</creatorcontrib><creatorcontrib>PARK, Yunju</creatorcontrib><creatorcontrib>HWANG, Sunghyun</creatorcontrib><creatorcontrib>JEONG, Byung Jang</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEICE Transactions on Communications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>PARK, Jaehyun</au><au>PARK, Yunju</au><au>HWANG, Sunghyun</au><au>JEONG, Byung Jang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Low-Complexity GSVD-Based Beamforming and Power Allocation for a Cognitive Radio Network</atitle><jtitle>IEICE Transactions on Communications</jtitle><addtitle>IEICE Trans. Commun.</addtitle><date>2012</date><risdate>2012</risdate><volume>E95.B</volume><issue>11</issue><spage>3536</spage><epage>3544</epage><pages>3536-3544</pages><issn>0916-8516</issn><eissn>1745-1345</eissn><abstract>In this paper, low-complexity generalized singular value decomposition (GSVD) based beamforming schemes are proposed for a cognitive radio (CR) network in which multiple secondary users (SUs) with multiple antennas coexist with multiple primary users (PUs). In general, optimal beamforming, which suppresses the interference caused at PUs to below a certain threshold and maximizes the signal-to-interference-plus-noise ratios (SINRs) of multiple SUs simultaneously, requires a complicated iterative optimization process. To overcome the computational complexity, we introduce a signal-to-leakage-plus-noise ratio (SLNR) maximizing beamforming scheme in which the weight can be obtained by using the GSVD algorithm, and does not require any iterations or matrix squaring operations. Here, to satisfy the leakage constraints at PUs, two linear methods, zero forcing (ZF) preprocessing and power allocation, are proposed.</abstract><cop>Tokyo</cop><pub>The Institute of Electronics, Information and Communication Engineers</pub><doi>10.1587/transcom.E95.B.3536</doi><tpages>9</tpages></addata></record> |
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| subjects | Allocations Antennas Applied sciences Beamforming Cognitive radio cognitive radio network Detection, estimation, filtering, equalization, prediction Exact sciences and technology GSVD algorithm Information, signal and communications theory Iterative methods multi-user MIMO Networks Optimization power allocation Preprocessing Radiocommunication specific techniques Radiocommunications Signal and communications theory Signal, noise Systems, networks and services of telecommunications Telecommunications Telecommunications and information theory Transmission and modulation (techniques and equipments) |
| title | Low-Complexity GSVD-Based Beamforming and Power Allocation for a Cognitive Radio Network |
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