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

Author

Listed:
  • 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
    Department of Electrical Engineering and Information Technology, Institute of Digital Communication, Ruhr University, 44801 Bochum, Germany)

  • 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)

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.

Suggested Citation

  • Hindavi Kishor Jadhav & Vinoth Babu Kumaravelu & Arthi Murugadass & Agbotiname Lucky Imoize & Poongundran Selvaprabhu & Arunkumar Chandrasekhar, 2023. "Intelligent Transmit Antenna Selection Schemes for High-Rate Fully Generalized Spatial Modulation," Future Internet, MDPI, vol. 15(8), pages 1-19, August.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:8:p:281-:d:1221546
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    References listed on IDEAS

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    1. Ioannis P. Chochliouros & Michail-Alexandros Kourtis & Anastasia S. Spiliopoulou & Pavlos Lazaridis & Zaharias Zaharis & Charilaos Zarakovitis & Anastasios Kourtis, 2021. "Energy Efficiency Concerns and Trends in Future 5G Network Infrastructures," Energies, MDPI, vol. 14(17), pages 1-14, August.
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