Intelligent Transmit Antenna Selection Schemes for High-Rate Fully Generalized Spatial Modulation

Hindavi Kishor Jadhav, Vinoth Babu Kumaravelu (), Arthi Murugadass (), Agbotiname Lucky Imoize, Poongundran Selvaprabhu and Arunkumar Chandrasekhar
Additional contact information
Hindavi Kishor Jadhav: Department of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, India
Vinoth Babu Kumaravelu: Department of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, India
Arthi Murugadass: Department of Computer Science and Engineering (AI & ML), Sreenivasa Institute of Technology and Management Studies, Chittoor 517127, India
Agbotiname Lucky Imoize: Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Lagos 100213, Nigeria
Poongundran Selvaprabhu: Department of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, India
Arunkumar Chandrasekhar: Department of Sensor and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, India

Future Internet, 2023, vol. 15, issue 8, 1-19

Abstract: 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), K -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 10 − 5 . 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.

Keywords: free distance optimized transmit antenna selection (FD-TAS); fully generalized spatial modulation (FGSM); machine learning (ML); support vector machine (SVM); transmit antenna selection (TAS) (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/15/8/281/pdf (application/pdf)
https://www.mdpi.com/1999-5903/15/8/281/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:15:y:2023:i:8:p:281-:d:1221546

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().