Low-Complexity Sphere Decoding of Polar Codes Based on Optimum Path Metric
Sphere decoding (SD) of polar codes is an efficient method to achieve the error performance of maximum likelihood (ML) decoding. But the complexity of the conventional sphere decoder is still high, where the candidates in a target sphere are enumerated and the radius is decreased gradually until no...
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Veröffentlicht in: | IEEE communications letters 2014-02, Vol.18 (2), p.332-335 |
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description | Sphere decoding (SD) of polar codes is an efficient method to achieve the error performance of maximum likelihood (ML) decoding. But the complexity of the conventional sphere decoder is still high, where the candidates in a target sphere are enumerated and the radius is decreased gradually until no available candidate is in the sphere. In order to reduce the complexity of SD, a stack SD (SSD) algorithm with an efficient enumeration is proposed in this paper. Based on a novel path metric, SSD can effectively narrow the search range when enumerating the candidates within a sphere. The proposed metric follows an exact ML rule and takes the full usage of the whole received sequence. Furthermore, another very simple metric is provided as an approximation of the ML metric in the high signal-to-noise ratio regime. For short polar codes, simulation results over the additive white Gaussian noise channels show that the complexity of SSD based on the proposed metrics is up to 100 times lower than that of the conventional SD. |
doi_str_mv | 10.1109/LCOMM.2014.010214.131826 |
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But the complexity of the conventional sphere decoder is still high, where the candidates in a target sphere are enumerated and the radius is decreased gradually until no available candidate is in the sphere. In order to reduce the complexity of SD, a stack SD (SSD) algorithm with an efficient enumeration is proposed in this paper. Based on a novel path metric, SSD can effectively narrow the search range when enumerating the candidates within a sphere. The proposed metric follows an exact ML rule and takes the full usage of the whole received sequence. Furthermore, another very simple metric is provided as an approximation of the ML metric in the high signal-to-noise ratio regime. For short polar codes, simulation results over the additive white Gaussian noise channels show that the complexity of SSD based on the proposed metrics is up to 100 times lower than that of the conventional SD.</description><identifier>ISSN: 1089-7798</identifier><identifier>EISSN: 1558-2558</identifier><identifier>DOI: 10.1109/LCOMM.2014.010214.131826</identifier><identifier>CODEN: ICLEF6</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Algorithms ; Applied sciences ; Approximation methods ; Coding, codes ; Complexity theory ; Exact sciences and technology ; Information, signal and communications theory ; Maximum likelihood decoding ; maximum likelihood rule ; Measurement ; Polar codes ; Signal and communications theory ; Signal to noise ratio ; sphere decoding ; successive cancellation decoding ; Telecommunications and information theory ; Vectors</subject><ispartof>IEEE communications letters, 2014-02, Vol.18 (2), p.332-335</ispartof><rights>2015 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Feb 2014</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c387t-e7b137c8afbaf76a4870586dbb54c18c317312bc8304b4d5e2d62ca6ec69dc593</citedby><cites>FETCH-LOGICAL-c387t-e7b137c8afbaf76a4870586dbb54c18c317312bc8304b4d5e2d62ca6ec69dc593</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6708139$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,781,785,797,27928,27929,54762</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6708139$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28402645$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Niu, Kai</creatorcontrib><creatorcontrib>Chen, Kai</creatorcontrib><creatorcontrib>Lin, Jiaru</creatorcontrib><title>Low-Complexity Sphere Decoding of Polar Codes Based on Optimum Path Metric</title><title>IEEE communications letters</title><addtitle>COML</addtitle><description>Sphere decoding (SD) of polar codes is an efficient method to achieve the error performance of maximum likelihood (ML) decoding. But the complexity of the conventional sphere decoder is still high, where the candidates in a target sphere are enumerated and the radius is decreased gradually until no available candidate is in the sphere. In order to reduce the complexity of SD, a stack SD (SSD) algorithm with an efficient enumeration is proposed in this paper. Based on a novel path metric, SSD can effectively narrow the search range when enumerating the candidates within a sphere. The proposed metric follows an exact ML rule and takes the full usage of the whole received sequence. Furthermore, another very simple metric is provided as an approximation of the ML metric in the high signal-to-noise ratio regime. For short polar codes, simulation results over the additive white Gaussian noise channels show that the complexity of SSD based on the proposed metrics is up to 100 times lower than that of the conventional SD.</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Approximation methods</subject><subject>Coding, codes</subject><subject>Complexity theory</subject><subject>Exact sciences and technology</subject><subject>Information, signal and communications theory</subject><subject>Maximum likelihood decoding</subject><subject>maximum likelihood rule</subject><subject>Measurement</subject><subject>Polar codes</subject><subject>Signal and communications theory</subject><subject>Signal to noise ratio</subject><subject>sphere decoding</subject><subject>successive cancellation decoding</subject><subject>Telecommunications and information theory</subject><subject>Vectors</subject><issn>1089-7798</issn><issn>1558-2558</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kEtPwzAQhCMEEqXwC7hYQhxTvHb8yBHCW6laCThbjuPQVEkd7FTQf08gVS87K-3MjvRFEQI8A8DpTZ4t5vMZwZDMMGAyCFCQhB9FE2BMxmQYx8OOZRoLkcrT6CyENcZYEgaT6DV333Hm2q6xP3W_Q2_dynqL7q1xZb35RK5CS9dojzJX2oDudLAlchu06Pq63bZoqfsVmtve1-Y8Oql0E-zFXqfRx-PDe_Yc54unl-w2jw2Voo-tKIAKI3VV6EpwnUiBmeRlUbDEgDQUBAVSGElxUiQls6TkxGhuDU9Lw1I6ja7Gv513X1sberV2W78ZKhUwzABSQWBwydFlvAvB20p1vm613ynA6o-c-ien_sipkZwayQ3R632BDkY3ldcbU4dDnsgEE56wwXc5-mpr7eHMBZZAU_oLGAZ2YA</recordid><startdate>20140201</startdate><enddate>20140201</enddate><creator>Niu, Kai</creator><creator>Chen, Kai</creator><creator>Lin, Jiaru</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>7SP</scope><scope>8FD</scope><scope>L7M</scope></search><sort><creationdate>20140201</creationdate><title>Low-Complexity Sphere Decoding of Polar Codes Based on Optimum Path Metric</title><author>Niu, Kai ; Chen, Kai ; Lin, Jiaru</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c387t-e7b137c8afbaf76a4870586dbb54c18c317312bc8304b4d5e2d62ca6ec69dc593</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Algorithms</topic><topic>Applied sciences</topic><topic>Approximation methods</topic><topic>Coding, codes</topic><topic>Complexity theory</topic><topic>Exact sciences and technology</topic><topic>Information, signal and communications theory</topic><topic>Maximum likelihood decoding</topic><topic>maximum likelihood rule</topic><topic>Measurement</topic><topic>Polar codes</topic><topic>Signal and communications theory</topic><topic>Signal to noise ratio</topic><topic>sphere decoding</topic><topic>successive cancellation decoding</topic><topic>Telecommunications and information theory</topic><topic>Vectors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Niu, Kai</creatorcontrib><creatorcontrib>Chen, Kai</creatorcontrib><creatorcontrib>Lin, Jiaru</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>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE communications letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Niu, Kai</au><au>Chen, Kai</au><au>Lin, Jiaru</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Low-Complexity Sphere Decoding of Polar Codes Based on Optimum Path Metric</atitle><jtitle>IEEE communications letters</jtitle><stitle>COML</stitle><date>2014-02-01</date><risdate>2014</risdate><volume>18</volume><issue>2</issue><spage>332</spage><epage>335</epage><pages>332-335</pages><issn>1089-7798</issn><eissn>1558-2558</eissn><coden>ICLEF6</coden><abstract>Sphere decoding (SD) of polar codes is an efficient method to achieve the error performance of maximum likelihood (ML) decoding. But the complexity of the conventional sphere decoder is still high, where the candidates in a target sphere are enumerated and the radius is decreased gradually until no available candidate is in the sphere. In order to reduce the complexity of SD, a stack SD (SSD) algorithm with an efficient enumeration is proposed in this paper. Based on a novel path metric, SSD can effectively narrow the search range when enumerating the candidates within a sphere. The proposed metric follows an exact ML rule and takes the full usage of the whole received sequence. Furthermore, another very simple metric is provided as an approximation of the ML metric in the high signal-to-noise ratio regime. For short polar codes, simulation results over the additive white Gaussian noise channels show that the complexity of SSD based on the proposed metrics is up to 100 times lower than that of the conventional SD.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/LCOMM.2014.010214.131826</doi><tpages>4</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Applied sciences Approximation methods Coding, codes Complexity theory Exact sciences and technology Information, signal and communications theory Maximum likelihood decoding maximum likelihood rule Measurement Polar codes Signal and communications theory Signal to noise ratio sphere decoding successive cancellation decoding Telecommunications and information theory Vectors |
title | Low-Complexity Sphere Decoding of Polar Codes Based on Optimum Path Metric |
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