# Intelligent Transmit Antenna Selection Schemes for High-Rate Fully Generalized Spatial Modulation
## 1 Introduction
### Related Work  
The future-generation network demands improvements in EE and SE [1]. The SE of SM is proportional to \(log_{2}(N_{tx})\), which lowers the performance of SM in terms of SE [9; 10]. To overcome this, high-rate versions of SM are developed [13]. FGSM is a high-rate variation of SM, where transmit antennas and SE are linearly related and could meet the expanding demands of 6G [13]. Lower-powered integrated circuits, antenna diversity, or a mix of the two, may be adopted to boost EE [18]. The traditional SM technique does not provide transmit diversity gain. TAS practices have been incorporated to boost the SM system's transmit diversity gain [14]. TAS approaches are applied to overcome the cost and complexity challenges in massive MIMO systems [12]. Different massive MIMO configurations have shown that antenna selection can improve performance and lower RF costs [19]. For optimal channel utilization, a novel SM technique is developed by fusing transmit mode switching and adaptive modulation techniques [20]. The free distance (FD) strategy is computationally more expensive due to the requirement of simultaneously searching for antenna pairs and constellation orders that maximize the least FD. Although FD-TAS provides the best EE performance, its implementation is more challenging. The FD-TAS system has gained popularity and is used as a benchmark in many TAS-related articles [3; 13; 14; 15; 16; 17; 21]. A capacity-optimized antenna selection (COAS) technique is addressed in conjunction with FD-TAS, where the antenna subset with the most prominent channel capacity is chosen [13; 14; 15; 17]. The correlation angle of two antennas is used to evaluate the TAS [13; 16]. In this study, the antenna subset with the least correlation is prioritized. The capacity and correlation angle-based technique is proposed to enhance ABER performance [13] while increasing computing complexity. A partitioning strategy that relies on the capacity and correlation angle further reduces the system's complexity [13]. Sub-optimal techniques are less efficient than FD-TAS when it comes to TAS in SM and its variants [13; 14; 15; 16; 17]. To save the computations offered by FD-TAS, nowadays data-driven approaches are utilized. TAS is a classification problem that can be solved using supervised ML algorithms. A pattern recognition-based approach is proposed for traditional MIMO [22]. This work is tested for 5000 samples with KNN and SVM algorithms. This work can be extended further to various supervised learning algorithms and larger datasets to boost the overall system performance. Two unique features of the channel space are the element norm of \(\mathbf{H}\) and element norm of \(\mathbf{H}^{H}\mathbf{H}\), which are used to analyze the performance of these algorithms. The absolute of elements of \(\mathbf{H}\) is used to analyze NB- and SVM-based TAS algorithms in conventional MIMO architecture [23]. The purpose of this research is to demonstrate how ML can be utilized to enhance the security of MIMO architecture. In the future, other ML algorithms could be employed to carry out this research. In this example, the algorithms are only trained on 10,000 samples; nevertheless, this number can be increased to improve overall efficiency. To solve power assignment problems in adaptive SM-MIMO, supervised ML algorithms and deep neural networks (DNNs) are proposed and implemented [24]. The ABER performance in this work can be enhanced by extending the training data beyond 2000 feature label pairs. Other features, such as the angle of elements of \(\mathbf{H}\), real and imaginary parts of elements of \(\mathbf{H}\), etc., can be used as attributes to obtain better results. TAS as a classification problem is addressed using DNN and SVM for SM [25]. To construct the models, the channel gain and correlation properties of the column space of \(\mathbf{H}\) are retrieved. In this work, the comparison with other ML-based algorithms is not conducted with DNN and SVM.