Joint Parallel Tabu Search Algorithm-Based Look-Up Table Design and Deep Learning-Based Signal Detection for OFDM-AIM
This letter first proposes a parallel Tabu Search (PTS) algorithm-based look-up table for orthogonal frequency division multiplexing with all index modulation (OFDM-AIM) called PTS-AIM. In the conventional OFDM-AIM (referred to as AIM in this letter), each subcarrier within a subblock is mapped to t...
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| Veröffentlicht in: | IEEE wireless communications letters 2024-02, Vol.13 (2), p.575-579 |
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| creator | Yildirim, Mahmut |
| description | This letter first proposes a parallel Tabu Search (PTS) algorithm-based look-up table for orthogonal frequency division multiplexing with all index modulation (OFDM-AIM) called PTS-AIM. In the conventional OFDM-AIM (referred to as AIM in this letter), each subcarrier within a subblock is mapped to the same quadrature amplitude modulation (QAM) constellation points, while in the PTS-AIM, the PTS algorithm searches the optimum constellation point for each subcarrier to improve the bit error rate (BER) performance. Secondly, a deep learning (DL)-based signal detector termed as DeepAIM is proposed, which combines long short-term memory (LSTM) algorithm and deep neural network (DNN). Finally, a novel architecture, PTS-DeepAIM, is designed, where PTS-AIM and DeepAIM schemes are considered together. The simulation results show that the proposed PTS-DeepAIM outperforms AIM in BER performance and computation time, thanks to the benefit of the unified PTS-based look-up table design and DL-based signal detection architecture. |
| doi_str_mv | 10.1109/LWC.2023.3342933 |
| format | Article |
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In the conventional OFDM-AIM (referred to as AIM in this letter), each subcarrier within a subblock is mapped to the same quadrature amplitude modulation (QAM) constellation points, while in the PTS-AIM, the PTS algorithm searches the optimum constellation point for each subcarrier to improve the bit error rate (BER) performance. Secondly, a deep learning (DL)-based signal detector termed as DeepAIM is proposed, which combines long short-term memory (LSTM) algorithm and deep neural network (DNN). Finally, a novel architecture, PTS-DeepAIM, is designed, where PTS-AIM and DeepAIM schemes are considered together. The simulation results show that the proposed PTS-DeepAIM outperforms AIM in BER performance and computation time, thanks to the benefit of the unified PTS-based look-up table design and DL-based signal detection architecture.</description><identifier>ISSN: 2162-2337</identifier><identifier>EISSN: 2162-2345</identifier><identifier>DOI: 10.1109/LWC.2023.3342933</identifier><identifier>CODEN: IWCLAF</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Artificial neural networks ; Bit error rate ; Deep learning ; deep learning (DL) ; deep neural network (DNN) ; Detectors ; Indexes ; long short-term memory (LSTM) ; Lookup tables ; Machine learning ; Orthogonal Frequency Division Multiplexing ; Orthogonal frequency division multiplexing with all index modulation (OFDM-AIM) ; parallel tabu search algorithm ; Quadrature amplitude modulation ; recurrent neural network (RNN) ; Search algorithms ; Signal detection ; Signal detectors ; Subcarriers ; Table lookup ; Tabu search</subject><ispartof>IEEE wireless communications letters, 2024-02, Vol.13 (2), p.575-579</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c245t-9bf14821cce427ffb3e831fc360bba664ea45411b109ff4a94a41323bc053b113</cites><orcidid>0000-0001-6012-2286</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10360207$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10360207$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Yildirim, Mahmut</creatorcontrib><title>Joint Parallel Tabu Search Algorithm-Based Look-Up Table Design and Deep Learning-Based Signal Detection for OFDM-AIM</title><title>IEEE wireless communications letters</title><addtitle>LWC</addtitle><description>This letter first proposes a parallel Tabu Search (PTS) algorithm-based look-up table for orthogonal frequency division multiplexing with all index modulation (OFDM-AIM) called PTS-AIM. 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The simulation results show that the proposed PTS-DeepAIM outperforms AIM in BER performance and computation time, thanks to the benefit of the unified PTS-based look-up table design and DL-based signal detection architecture.</description><subject>Artificial neural networks</subject><subject>Bit error rate</subject><subject>Deep learning</subject><subject>deep learning (DL)</subject><subject>deep neural network (DNN)</subject><subject>Detectors</subject><subject>Indexes</subject><subject>long short-term memory (LSTM)</subject><subject>Lookup tables</subject><subject>Machine learning</subject><subject>Orthogonal Frequency Division Multiplexing</subject><subject>Orthogonal frequency division multiplexing with all index modulation (OFDM-AIM)</subject><subject>parallel tabu search algorithm</subject><subject>Quadrature amplitude modulation</subject><subject>recurrent neural network (RNN)</subject><subject>Search algorithms</subject><subject>Signal detection</subject><subject>Signal detectors</subject><subject>Subcarriers</subject><subject>Table lookup</subject><subject>Tabu search</subject><issn>2162-2337</issn><issn>2162-2345</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkEFPAjEQhRujiQS5e_DQxPNi2-ku7BFBFLMEEyAem7a0sLhssd09-O8tgRjnMpO89yYvH0L3lPQpJflT8TnuM8KgD8BZDnCFOoxmLGHA0-u_Gwa3qBfCnsTJCGV02EHtuyvrBn9IL6vKVHglVYuXRnq9w6Nq63zZ7A7JswxmgwvnvpL18eSpDJ6YUG5rLOtNPM0RFzFUl_X2Yl5GUVZRaoxuSldj6zxeTCfzZDSb36EbK6tgepfdRevpy2r8lhSL19l4VCSa8bRJcmUpHzKqteFsYK0CMwRqNWREKZll3EieckpVZGAtlzmXnAIDpUkKilLoosfz36N3360Jjdi71sdeQbCccQJpGml1ETm7tHcheGPF0ZcH6X8EJeLEV0S-4sRXXPjGyMM5Uhpj_tljM0YG8AurlHRB</recordid><startdate>20240201</startdate><enddate>20240201</enddate><creator>Yildirim, Mahmut</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0001-6012-2286</orcidid></search><sort><creationdate>20240201</creationdate><title>Joint Parallel Tabu Search Algorithm-Based Look-Up Table Design and Deep Learning-Based Signal Detection for OFDM-AIM</title><author>Yildirim, Mahmut</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c245t-9bf14821cce427ffb3e831fc360bba664ea45411b109ff4a94a41323bc053b113</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Artificial neural networks</topic><topic>Bit error rate</topic><topic>Deep learning</topic><topic>deep learning (DL)</topic><topic>deep neural network (DNN)</topic><topic>Detectors</topic><topic>Indexes</topic><topic>long short-term memory (LSTM)</topic><topic>Lookup tables</topic><topic>Machine learning</topic><topic>Orthogonal Frequency Division Multiplexing</topic><topic>Orthogonal frequency division multiplexing with all index modulation (OFDM-AIM)</topic><topic>parallel tabu search algorithm</topic><topic>Quadrature amplitude modulation</topic><topic>recurrent neural network (RNN)</topic><topic>Search algorithms</topic><topic>Signal detection</topic><topic>Signal detectors</topic><topic>Subcarriers</topic><topic>Table lookup</topic><topic>Tabu search</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yildirim, Mahmut</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>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE wireless communications letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yildirim, Mahmut</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Joint Parallel Tabu Search Algorithm-Based Look-Up Table Design and Deep Learning-Based Signal Detection for OFDM-AIM</atitle><jtitle>IEEE wireless communications letters</jtitle><stitle>LWC</stitle><date>2024-02-01</date><risdate>2024</risdate><volume>13</volume><issue>2</issue><spage>575</spage><epage>579</epage><pages>575-579</pages><issn>2162-2337</issn><eissn>2162-2345</eissn><coden>IWCLAF</coden><abstract>This letter first proposes a parallel Tabu Search (PTS) algorithm-based look-up table for orthogonal frequency division multiplexing with all index modulation (OFDM-AIM) called PTS-AIM. In the conventional OFDM-AIM (referred to as AIM in this letter), each subcarrier within a subblock is mapped to the same quadrature amplitude modulation (QAM) constellation points, while in the PTS-AIM, the PTS algorithm searches the optimum constellation point for each subcarrier to improve the bit error rate (BER) performance. Secondly, a deep learning (DL)-based signal detector termed as DeepAIM is proposed, which combines long short-term memory (LSTM) algorithm and deep neural network (DNN). Finally, a novel architecture, PTS-DeepAIM, is designed, where PTS-AIM and DeepAIM schemes are considered together. The simulation results show that the proposed PTS-DeepAIM outperforms AIM in BER performance and computation time, thanks to the benefit of the unified PTS-based look-up table design and DL-based signal detection architecture.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/LWC.2023.3342933</doi><tpages>5</tpages><orcidid>https://orcid.org/0000-0001-6012-2286</orcidid></addata></record> |
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| issn | 2162-2337 2162-2345 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Artificial neural networks Bit error rate Deep learning deep learning (DL) deep neural network (DNN) Detectors Indexes long short-term memory (LSTM) Lookup tables Machine learning Orthogonal Frequency Division Multiplexing Orthogonal frequency division multiplexing with all index modulation (OFDM-AIM) parallel tabu search algorithm Quadrature amplitude modulation recurrent neural network (RNN) Search algorithms Signal detection Signal detectors Subcarriers Table lookup Tabu search |
| title | Joint Parallel Tabu Search Algorithm-Based Look-Up Table Design and Deep Learning-Based Signal Detection for OFDM-AIM |
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