Signal Detection in MIMO-OFDM Systems Based on SSDE Algorithm
Device-to-device communication enables to improve the application performance of multi-input multi-output (MIMO) technology. In a multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system, its performance is largely reflected by the signal detection algorithm used in rec...
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| Vydáno v: | Wireless personal communications Ročník 82; číslo 4; s. 2709 - 2725 |
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01.06.2015
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| ISSN: | 0929-6212, 1572-834X |
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| Abstract | Device-to-device communication enables to improve the application performance of multi-input multi-output (MIMO) technology. In a multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system, its performance is largely reflected by the signal detection algorithm used in receiver. As a sub-optimal maximum likelihood (ML) detection method, the Selective Spanning with Fast Enumeration algorithm can be successfully applied in MIMO-OFDM systems with high-order modulation. However, its Fast Enumeration scheme calculates constellation points based on fixed formula, which tends to yield pseudo constellation points outside of constellation maps, and consequently cannot work well in low-order modulation. To address this, a selective spanning with direct enumeration (SSDE) algorithm is proposed in this paper. Simulation results proved that the SSDE can achieve similar detection performance at a much lower computational cost in comparison with the ML method. The performance in terms of bit error rate (BER) obtained by SSDE method is also superior to those from the Minimum mean square error and Zero forcing detection algorithms with a huge savings in computational load. By adjusting the parameters used in the SSDE, the tradeoff between BER and computation complexity can be flexibly changed to satisfy specific design requirements in different applications. |
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| AbstractList | Device-to-device communication enables to improve the application performance of multi-input multi-output (MIMO) technology. In a multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system, its performance is largely reflected by the signal detection algorithm used in receiver. As a sub-optimal maximum likelihood (ML) detection method, the Selective Spanning with Fast Enumeration algorithm can be successfully applied in MIMO-OFDM systems with high-order modulation. However, its Fast Enumeration scheme calculates constellation points based on fixed formula, which tends to yield pseudo constellation points outside of constellation maps, and consequently cannot work well in low-order modulation. To address this, a selective spanning with direct enumeration (SSDE) algorithm is proposed in this paper. Simulation results proved that the SSDE can achieve similar detection performance at a much lower computational cost in comparison with the ML method. The performance in terms of bit error rate (BER) obtained by SSDE method is also superior to those from the Minimum mean square error and Zero forcing detection algorithms with a huge savings in computational load. By adjusting the parameters used in the SSDE, the tradeoff between BER and computation complexity can be flexibly changed to satisfy specific design requirements in different applications. |
| Author | Hu, Fengye Ma, Kai Wang, Lu Du, Yu Cen, Ling |
| Author_xml | – sequence: 1 givenname: Fengye surname: Hu fullname: Hu, Fengye organization: College of Communication Engineering, Jilin University – sequence: 2 givenname: Yu surname: Du fullname: Du, Yu email: yudu13@mails.jlu.edu.cn organization: College of Communication Engineering, Jilin University – sequence: 3 givenname: Ling surname: Cen fullname: Cen, Ling organization: College of Communication Engineering, Jilin University – sequence: 4 givenname: Kai surname: Ma fullname: Ma, Kai organization: College of Communication Engineering, Jilin University – sequence: 5 givenname: Lu surname: Wang fullname: Wang, Lu organization: College of Communication Engineering, Jilin University |
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| Cites_doi | 10.1109/TCSII.2009.2019331 10.1109/TSP.2008.928965 10.1109/TIT.2004.828067 10.1007/s11265-010-0499-0 10.1049/el:20010899 10.1109/VETECF.2003.1285069 10.1109/TCOMM.2008.050623 10.1109/TVLSI.2007.893621 10.1109/18.771234 10.1109/ICSAMOS.2009.5289228 10.1109/JSAC.2012.120117 10.1109/TCSI.2010.2043017 10.1109/LSP.2004.833522 10.1109/TIT.2007.915976 10.1109/ICC.2008.144 10.1109/JSAC.2005.862402 10.1109/TCOMM.2005.851590 10.1109/TWC.2004.842951 |
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| Keywords | ML detection MIMO-OFDM SSDE Sub-optimal ML detection |
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Novel energy-efficient scalable soft-output SSFE MIMO detector architectures. In International Symposium on Systems, Architectures, Modeling, and Simulation (pp. 165–171). – reference: BerenguerIWangXDMIMO antenna selection with lattice reduction aided linear receiversIEEE Transactions on Vehicular Techology200453512981302 – reference: ChenSZZhangTXinYRelaxed K-best MIMO signal detector design and VLSI implementationIEEE Transactions on Very Large Scale Integation (VLSI) Systems200715332833710.1109/TVLSI.2007.893621 – reference: Maddah-AliMSadrabadiMBroadcast in MIMO systems based on a generalized QR decomposition: Signaling and performance analysisIEEE Transactions on Information Theory20085431124113810.1109/TIT.2007.9159762445055 – reference: ShenCAEltawilAMA radius adaptive K-best decoder with early termination: Algorithm and VLSI architectureIEEE Transactions on Circuits and Systems I20105792476248610.1109/TCSI.2010.20430172814170 – reference: FasthuberRLiMNovoDRaghavanPExploration of soft-output MIMO detector implementations on massive parallel processorsJournal of Signal Processing Systems2011641759210.1007/s11265-010-0499-0 – reference: 3GPP (2012). Feasibility study for proximity services (prose), In 3GPP, Technical Report; TR 22.803 V0.2.0. – reference: PhamDPattipatiKWillettPLuoJAn improved complex sphere decoder for V-BLAST systemsIEEE Signal Processing Letters200411974875110.1109/LSP.2004.833522 – reference: WubbenDRohnkeJEfficient algorithm for decoding layered space-time codesIEEE Electronic Letters200137221348135010.1049/el:20010899 – reference: Wubben D., Rohnke J., Kuhn V., & Kammeyer, K. D. (2003). MMSE extension of V-BLAST based on sorted QR decomposition. In IEEE 58th Vehicular Technology Conference (Vol. 1 pp. 508–512). – reference: ZhanGPeterNAlgorithm and implementation of the K-best sphere decoding for MIMO detectionIEEE Journal on Selected Areas in Communications200624349150310.1109/JSAC.2005.862402 – reference: TaeHIParkIKimJYiJA new signal detection method for spatially multiplexed MIMO systems and its VLSI implementationIEEE Transactions on Circuits and Systems II200956539940310.1109/TCSII.2009.2019331 – reference: SongLHanZZhangZJiaoBNon-cooperative feedback-rate control game for channel state information in wireless networksSelected Areas in Communications, IEEE Journal on201230118819710.1109/JSAC.2012.120117 – reference: Li, M., & Bougard, B., et al. (2008). Selective spanning with fast enumeration: A near maximum-likelihood MIMO detector designed for parallel programmable baseband architectures. In IEEE International Conference on Communications (pp. 737–741). – reference: LiuDNFitzMPLow complexity affine MMSE detector for iterative detection-decoding MIMO OFDM systemsIEEE Transactions on Communications200856115015810.1109/TCOMM.2008.050623 – reference: ViterboEBoutrosJUniversal lattice decoder for fading channelIEEE Transactions on Information Theory19995451639164210.1109/18.7712341699088 – volume: 56 start-page: 399 issue: 5 year: 2009 ident: 2374_CR16 publication-title: IEEE Transactions on Circuits and Systems II doi: 10.1109/TCSII.2009.2019331 – volume: 56 start-page: 5191 issue: 10 year: 2008 ident: 2374_CR4 publication-title: IEEE Transactions on Signal Processing doi: 10.1109/TSP.2008.928965 – volume: 50 start-page: 968 issue: 6 year: 2004 ident: 2374_CR11 publication-title: IEEE Transactions on Information Theory doi: 10.1109/TIT.2004.828067 – volume: 64 start-page: 75 issue: 1 year: 2011 ident: 2374_CR19 publication-title: Journal of Signal Processing Systems doi: 10.1007/s11265-010-0499-0 – volume: 37 start-page: 1348 issue: 22 year: 2001 ident: 2374_CR6 publication-title: IEEE Electronic Letters doi: 10.1049/el:20010899 – ident: 2374_CR7 doi: 10.1109/VETECF.2003.1285069 – ident: 2374_CR1 – volume: 56 start-page: 150 issue: 1 year: 2008 ident: 2374_CR3 publication-title: IEEE Transactions on Communications doi: 10.1109/TCOMM.2008.050623 – volume: 15 start-page: 328 issue: 3 year: 2007 ident: 2374_CR14 publication-title: IEEE Transactions on Very Large Scale Integation (VLSI) Systems doi: 10.1109/TVLSI.2007.893621 – volume: 54 start-page: 1639 issue: 5 year: 1999 ident: 2374_CR8 publication-title: IEEE Transactions on Information Theory doi: 10.1109/18.771234 – ident: 2374_CR18 doi: 10.1109/ICSAMOS.2009.5289228 – volume: 30 start-page: 188 issue: 1 year: 2012 ident: 2374_CR2 publication-title: Selected Areas in Communications, IEEE Journal on doi: 10.1109/JSAC.2012.120117 – volume: 57 start-page: 2476 issue: 9 year: 2010 ident: 2374_CR15 publication-title: IEEE Transactions on Circuits and Systems I doi: 10.1109/TCSI.2010.2043017 – volume: 53 start-page: 1298 issue: 5 year: 2004 ident: 2374_CR20 publication-title: IEEE Transactions on Vehicular Techology – volume: 11 start-page: 748 issue: 9 year: 2004 ident: 2374_CR10 publication-title: IEEE Signal Processing Letters doi: 10.1109/LSP.2004.833522 – volume: 54 start-page: 1124 issue: 3 year: 2008 ident: 2374_CR5 publication-title: IEEE Transactions on Information Theory doi: 10.1109/TIT.2007.915976 – ident: 2374_CR17 doi: 10.1109/ICC.2008.144 – volume: 24 start-page: 491 issue: 3 year: 2006 ident: 2374_CR13 publication-title: IEEE Journal on Selected Areas in Communications doi: 10.1109/JSAC.2005.862402 – volume: 54 start-page: 1104 issue: 7 year: 2005 ident: 2374_CR9 publication-title: IEEE Transactions on Communications doi: 10.1109/TCOMM.2005.851590 – volume: 4 start-page: 710 issue: 2 year: 2005 ident: 2374_CR12 publication-title: IEEE Transactions on Wireless Communications doi: 10.1109/TWC.2004.842951 |
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| Title | Signal Detection in MIMO-OFDM Systems Based on SSDE Algorithm |
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