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

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.