Intelligent Transmit Antenna Selection Schemes for High-Rate Fully Generalized Spatial Modulation
The sixth-generation (6G) network is supposed to transmit significantly more data at much quicker rates than existing networks while meeting severe energy efficiency (EE) targets. The high-rate spatial modulation (SM) methods can be used to deal with these design metrics. SM uses transmit antenna se...
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| Format: | Article |
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MDPI AG
2023-08-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/15/8/281 |
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| author | Hindavi Kishor Jadhav Vinoth Babu Kumaravelu Arthi Murugadass Agbotiname Lucky Imoize Poongundran Selvaprabhu Arunkumar Chandrasekhar |
| author_facet | Hindavi Kishor Jadhav Vinoth Babu Kumaravelu Arthi Murugadass Agbotiname Lucky Imoize Poongundran Selvaprabhu Arunkumar Chandrasekhar |
| author_sort | Hindavi Kishor Jadhav |
| collection | DOAJ |
| description | The sixth-generation (6G) network is supposed to transmit significantly more data at much quicker rates than existing networks while meeting severe energy efficiency (EE) targets. The high-rate spatial modulation (SM) methods can be used to deal with these design metrics. SM uses transmit antenna selection (TAS) practices to improve the EE of the network. Although it is computationally intensive, free distance optimized TAS (FD-TAS) is the best for performing the average bit error rate (ABER). The present investigation aims to examine the effectiveness of various machine learning (ML)-assisted TAS practices, such as support vector machine (SVM), naïve Bayes (NB), <i>K</i>-nearest neighbor (KNN), and decision tree (DT), to the small-scale multiple-input multiple-output (MIMO)-based fully generalized spatial modulation (FGSM) system. To the best of our knowledge, there is no ML-based antenna selection schemes for high-rate FGSM. SVM-based TAS schemes achieve ∼71.1% classification accuracy, outperforming all other approaches. The ABER performance of each scheme is evaluated using a higher constellation order, along with various transmit antennas to achieve the target ABER of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mn>10</mn><mrow><mo>−</mo><mn>5</mn></mrow></msup></semantics></math></inline-formula>. By employing SVM for TAS, FGSM can achieve a minimal gain of ∼2.2 dB over FGSM without TAS (FGSM-NTAS). All TAS strategies based on ML perform better than FGSM-NTAS. |
| first_indexed | 2024-03-10T23:55:34Z |
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| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-10T23:55:34Z |
| publishDate | 2023-08-01 |
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| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-b1e41878d5614971810579155f2f4ee82023-11-19T01:12:35ZengMDPI AGFuture Internet1999-59032023-08-0115828110.3390/fi15080281Intelligent Transmit Antenna Selection Schemes for High-Rate Fully Generalized Spatial ModulationHindavi Kishor Jadhav0Vinoth Babu Kumaravelu1Arthi Murugadass2Agbotiname Lucky Imoize3Poongundran Selvaprabhu4Arunkumar Chandrasekhar5Department of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, IndiaDepartment of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, IndiaDepartment of Computer Science and Engineering (AI & ML), Sreenivasa Institute of Technology and Management Studies, Chittoor 517127, IndiaDepartment of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Lagos 100213, NigeriaDepartment of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, IndiaDepartment of Sensor and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, IndiaThe sixth-generation (6G) network is supposed to transmit significantly more data at much quicker rates than existing networks while meeting severe energy efficiency (EE) targets. The high-rate spatial modulation (SM) methods can be used to deal with these design metrics. SM uses transmit antenna selection (TAS) practices to improve the EE of the network. Although it is computationally intensive, free distance optimized TAS (FD-TAS) is the best for performing the average bit error rate (ABER). The present investigation aims to examine the effectiveness of various machine learning (ML)-assisted TAS practices, such as support vector machine (SVM), naïve Bayes (NB), <i>K</i>-nearest neighbor (KNN), and decision tree (DT), to the small-scale multiple-input multiple-output (MIMO)-based fully generalized spatial modulation (FGSM) system. To the best of our knowledge, there is no ML-based antenna selection schemes for high-rate FGSM. SVM-based TAS schemes achieve ∼71.1% classification accuracy, outperforming all other approaches. The ABER performance of each scheme is evaluated using a higher constellation order, along with various transmit antennas to achieve the target ABER of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mn>10</mn><mrow><mo>−</mo><mn>5</mn></mrow></msup></semantics></math></inline-formula>. By employing SVM for TAS, FGSM can achieve a minimal gain of ∼2.2 dB over FGSM without TAS (FGSM-NTAS). All TAS strategies based on ML perform better than FGSM-NTAS.https://www.mdpi.com/1999-5903/15/8/281free distance optimized transmit antenna selection (FD-TAS)fully generalized spatial modulation (FGSM)machine learning (ML)support vector machine (SVM)transmit antenna selection (TAS) |
| spellingShingle | Hindavi Kishor Jadhav Vinoth Babu Kumaravelu Arthi Murugadass Agbotiname Lucky Imoize Poongundran Selvaprabhu Arunkumar Chandrasekhar Intelligent Transmit Antenna Selection Schemes for High-Rate Fully Generalized Spatial Modulation Future Internet free distance optimized transmit antenna selection (FD-TAS) fully generalized spatial modulation (FGSM) machine learning (ML) support vector machine (SVM) transmit antenna selection (TAS) |
| title | Intelligent Transmit Antenna Selection Schemes for High-Rate Fully Generalized Spatial Modulation |
| title_full | Intelligent Transmit Antenna Selection Schemes for High-Rate Fully Generalized Spatial Modulation |
| title_fullStr | Intelligent Transmit Antenna Selection Schemes for High-Rate Fully Generalized Spatial Modulation |
| title_full_unstemmed | Intelligent Transmit Antenna Selection Schemes for High-Rate Fully Generalized Spatial Modulation |
| title_short | Intelligent Transmit Antenna Selection Schemes for High-Rate Fully Generalized Spatial Modulation |
| title_sort | intelligent transmit antenna selection schemes for high rate fully generalized spatial modulation |
| topic | free distance optimized transmit antenna selection (FD-TAS) fully generalized spatial modulation (FGSM) machine learning (ML) support vector machine (SVM) transmit antenna selection (TAS) |
| url | https://www.mdpi.com/1999-5903/15/8/281 |
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