Upgraded Max-Log-MAP algorithm using adaptive correction factors for decoders in AWGN and fading channel
Max-Log-MAP (MLMAP) algorithm is a sub-optimal turbo decoding algorithm. There are two distortions which result in the sub-optimal performance of MLMAP algorithm. They are the optimistic effect of reliability values and connection between the intrinsic and extrinsic information. Of the two distortio...
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Veröffentlicht in: | Journal of ambient intelligence and humanized computing 2022-06, Vol.13 (6), p.3245-3255 |
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description | Max-Log-MAP (MLMAP) algorithm is a sub-optimal turbo decoding algorithm. There are two distortions which result in the sub-optimal performance of MLMAP algorithm. They are the optimistic effect of reliability values and connection between the intrinsic and extrinsic information. Of the two distortions, the decoding performance is primarily affected due to over-optimistic effect but slightly due to the correlation effect. This paper focusses on reducing the overestimation of reliability values, which depends on SNR. An improved method to enhance the error-correcting performance of MLMAP turbo decoding algorithm is presented. The proposed Max-Log-MAP with Double Optimized Correction Factor (DOCF-MLMAP) turbo decoding algorithm, overcomes the over-optimistic estimation of reliability values that degrade the performance of MLMAP algorithm utilizing a correction factor. A pair of appropriate correction factors (CF) scales the extrinsic information exchanged in every iteration, between the constituent decoders. The selection of correction factors is dependent on Signal to Noise Ratio (SNR). The CFs of both inner and outer decoders are optimized to a lowest Bit Error Rate (BER) for improved performance. From the BER results, it was observed that DOCF-MLMAP is better in performance than MLMAP. DOCF-MLMAP algorithm reaches a BER as low as 1 × 10
–6
at 12 dB in AWGN channel. The proposed DOCF-MLMAP algorithm proves to be superior in performance to the former algorithms in fading channel as well. The algorithms were also analyzed for various CODEC parameters. |
doi_str_mv | 10.1007/s12652-021-03160-6 |
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–6
at 12 dB in AWGN channel. The proposed DOCF-MLMAP algorithm proves to be superior in performance to the former algorithms in fading channel as well. The algorithms were also analyzed for various CODEC parameters.</description><identifier>ISSN: 1868-5137</identifier><identifier>EISSN: 1868-5145</identifier><identifier>DOI: 10.1007/s12652-021-03160-6</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Adaptive algorithms ; Algorithms ; Approximation ; Artificial Intelligence ; Bit error rate ; Business metrics ; Codec ; Codes ; Computational Intelligence ; Decoders ; Decoding ; Engineering ; Error correction ; Error correction & detection ; Fading ; Optimization ; Original Research ; Performance degradation ; Reliability ; Robotics and Automation ; Signal to noise ratio ; User Interfaces and Human Computer Interaction</subject><ispartof>Journal of ambient intelligence and humanized computing, 2022-06, Vol.13 (6), p.3245-3255</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021</rights><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c270t-2db4f8fa79d2bd93d874e6005d4b529e7870e6f715f11d47b1d9bbe2fe861f953</cites><orcidid>0000-0002-9178-5442</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s12652-021-03160-6$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2919505523?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,777,781,21369,27905,27906,33725,41469,42538,43786,51300,64364,64368,72218</link.rule.ids></links><search><creatorcontrib>Pradeep, N. S.</creatorcontrib><creatorcontrib>Aarthi, V.</creatorcontrib><creatorcontrib>Dhulipala, V. R. Sarma</creatorcontrib><title>Upgraded Max-Log-MAP algorithm using adaptive correction factors for decoders in AWGN and fading channel</title><title>Journal of ambient intelligence and humanized computing</title><addtitle>J Ambient Intell Human Comput</addtitle><description>Max-Log-MAP (MLMAP) algorithm is a sub-optimal turbo decoding algorithm. There are two distortions which result in the sub-optimal performance of MLMAP algorithm. They are the optimistic effect of reliability values and connection between the intrinsic and extrinsic information. Of the two distortions, the decoding performance is primarily affected due to over-optimistic effect but slightly due to the correlation effect. This paper focusses on reducing the overestimation of reliability values, which depends on SNR. An improved method to enhance the error-correcting performance of MLMAP turbo decoding algorithm is presented. The proposed Max-Log-MAP with Double Optimized Correction Factor (DOCF-MLMAP) turbo decoding algorithm, overcomes the over-optimistic estimation of reliability values that degrade the performance of MLMAP algorithm utilizing a correction factor. A pair of appropriate correction factors (CF) scales the extrinsic information exchanged in every iteration, between the constituent decoders. The selection of correction factors is dependent on Signal to Noise Ratio (SNR). The CFs of both inner and outer decoders are optimized to a lowest Bit Error Rate (BER) for improved performance. From the BER results, it was observed that DOCF-MLMAP is better in performance than MLMAP. DOCF-MLMAP algorithm reaches a BER as low as 1 × 10
–6
at 12 dB in AWGN channel. The proposed DOCF-MLMAP algorithm proves to be superior in performance to the former algorithms in fading channel as well. The algorithms were also analyzed for various CODEC parameters.</description><subject>Adaptive algorithms</subject><subject>Algorithms</subject><subject>Approximation</subject><subject>Artificial Intelligence</subject><subject>Bit error rate</subject><subject>Business metrics</subject><subject>Codec</subject><subject>Codes</subject><subject>Computational Intelligence</subject><subject>Decoders</subject><subject>Decoding</subject><subject>Engineering</subject><subject>Error correction</subject><subject>Error correction & detection</subject><subject>Fading</subject><subject>Optimization</subject><subject>Original Research</subject><subject>Performance degradation</subject><subject>Reliability</subject><subject>Robotics and Automation</subject><subject>Signal to noise ratio</subject><subject>User Interfaces and Human Computer Interaction</subject><issn>1868-5137</issn><issn>1868-5145</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kMFKAzEQhoMoWGpfwFPAczSTbJLdYylahVY9WDyG7CbZbmk3a7IVfXu3VvTmXGYGvm8GfoQugV4DpeomAZOCEcqAUA6SEnmCRpDLnAjIxOnvzNU5mqS0oUPxggPACK1XXR2NdRYvzQdZhJosp8_YbOsQm369w_vUtDU21nR98-5wFWJ0Vd-EFntT9SEm7EPE1lXBumFpWjx9nT9i09oBsAe3Wpu2ddsLdObNNrnJTx-j1d3ty-yeLJ7mD7PpglRM0Z4wW2Y-90YVlpW24DZXmZOUCpuVghVO5Yo66RUID2AzVYItytIx73IJvhB8jK6Od7sY3vYu9XoT9rEdXmpWQCGoEIwPFDtSVQwpRed1F5udiZ8aqD6Eqo-h6iFU_R2qloPEj1Ia4LZ28e_0P9YXCGN5jA</recordid><startdate>20220601</startdate><enddate>20220601</enddate><creator>Pradeep, N. 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Sarma</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c270t-2db4f8fa79d2bd93d874e6005d4b529e7870e6f715f11d47b1d9bbe2fe861f953</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Adaptive algorithms</topic><topic>Algorithms</topic><topic>Approximation</topic><topic>Artificial Intelligence</topic><topic>Bit error rate</topic><topic>Business metrics</topic><topic>Codec</topic><topic>Codes</topic><topic>Computational Intelligence</topic><topic>Decoders</topic><topic>Decoding</topic><topic>Engineering</topic><topic>Error correction</topic><topic>Error correction & detection</topic><topic>Fading</topic><topic>Optimization</topic><topic>Original Research</topic><topic>Performance degradation</topic><topic>Reliability</topic><topic>Robotics and Automation</topic><topic>Signal to noise ratio</topic><topic>User Interfaces and Human Computer Interaction</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pradeep, N. 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S.</au><au>Aarthi, V.</au><au>Dhulipala, V. R. Sarma</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Upgraded Max-Log-MAP algorithm using adaptive correction factors for decoders in AWGN and fading channel</atitle><jtitle>Journal of ambient intelligence and humanized computing</jtitle><stitle>J Ambient Intell Human Comput</stitle><date>2022-06-01</date><risdate>2022</risdate><volume>13</volume><issue>6</issue><spage>3245</spage><epage>3255</epage><pages>3245-3255</pages><issn>1868-5137</issn><eissn>1868-5145</eissn><abstract>Max-Log-MAP (MLMAP) algorithm is a sub-optimal turbo decoding algorithm. There are two distortions which result in the sub-optimal performance of MLMAP algorithm. They are the optimistic effect of reliability values and connection between the intrinsic and extrinsic information. Of the two distortions, the decoding performance is primarily affected due to over-optimistic effect but slightly due to the correlation effect. This paper focusses on reducing the overestimation of reliability values, which depends on SNR. An improved method to enhance the error-correcting performance of MLMAP turbo decoding algorithm is presented. The proposed Max-Log-MAP with Double Optimized Correction Factor (DOCF-MLMAP) turbo decoding algorithm, overcomes the over-optimistic estimation of reliability values that degrade the performance of MLMAP algorithm utilizing a correction factor. A pair of appropriate correction factors (CF) scales the extrinsic information exchanged in every iteration, between the constituent decoders. The selection of correction factors is dependent on Signal to Noise Ratio (SNR). The CFs of both inner and outer decoders are optimized to a lowest Bit Error Rate (BER) for improved performance. From the BER results, it was observed that DOCF-MLMAP is better in performance than MLMAP. DOCF-MLMAP algorithm reaches a BER as low as 1 × 10
–6
at 12 dB in AWGN channel. The proposed DOCF-MLMAP algorithm proves to be superior in performance to the former algorithms in fading channel as well. The algorithms were also analyzed for various CODEC parameters.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s12652-021-03160-6</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-9178-5442</orcidid></addata></record> |
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subjects | Adaptive algorithms Algorithms Approximation Artificial Intelligence Bit error rate Business metrics Codec Codes Computational Intelligence Decoders Decoding Engineering Error correction Error correction & detection Fading Optimization Original Research Performance degradation Reliability Robotics and Automation Signal to noise ratio User Interfaces and Human Computer Interaction |
title | Upgraded Max-Log-MAP algorithm using adaptive correction factors for decoders in AWGN and fading channel |
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