## 2 Related Works  
Next-generation networks necessitate improvements in EE and SE [1; 2; 3; 4; 5]. The SE of SM has a logarithmic relationship with the number of transmit antennas, which degrades the SE performance of SM [8; 9; 10; 11; 12]. In [8; 9; 10; 11; 12], high-rate variants of SM are implemented to combat this issue. There are a number of potential variants of SM, such as enhanced spatial modulation (ESM) [8], generalized spatial modulation (GSM) [9], modified spatial modulation (MSM) [10], quadrature spatial modulation (QSM) [11], FGSM [12], and FQSM [12], etc. Transmit antennas and SE in FGSM and FQSM are linearly related, which satisfies the increasing SE requirements of 6G [12]. As the conventional SM does not provide transmit diversity gain [13; 14; 15; 17], TAS schemes are incorporated to enhance transmit diversity. In [13; 14; 15; 17], distinct open- and closed-loop TAS schemes for SM are introduced. Although EDAS provides the best ABER performance, its implementation is a challenging task. By combining transmit mode switching with adaptive modulation, a unique SM approach that maximizes channel utilization has been presented in [18]. This technique selects the subset of antennas with the lowest Euclidean distance (ED) between symbols. It is more computationally intensive due to the necessity of simultaneously finding antenna pairs and an order of constellation that maximizes minimum ED. To resolve the issue of computational difficulty, antennas are selected on the basis of simple parameters such as channel gain, correlation angle, channel capacity, etc. The antenna subset with the highest channel capacity is selected using the capacity optimized antenna selection (COAS) approach [12; 15]. When compared to EDAS, the performance of a TAS scheme that is based on COAS is found to be subpar. A TAS scheme based on correlation angle is discussed in [12; 15], which evaluates the correlation angle between two antennas. The pair of antennas with the lowest correlation will be given precedence over the others. In [12; 15], a capacity and correlation angle-based TAS strategy is suggested for improving ABER performance. This method can improve ABER performance at the cost of a noticeable increase in computational difficulty. Moreover, the system's complexity is decreased by using a splitting strategy determined by capacity and correlation angle [12; 15]. According to [15], EDAS is more effective than sub-optimal TAS approaches in SM and its variants. Furthermore, TAS schemes are incorporated in [12] to increase the transmit diversity gains of advanced variants of SM. In [12], the computational cost of EDAS is compared to other sub-optimal TAS approaches for FGSM in terms of real valued multiplications. According to this, the design of the EDAS approach is overly complex when compared to other methods. In order to sidestep such tedious operations, a novel TAS method based on pattern recognition is presented in [19]. Here, support vector machine (SVM) and \(K\)-nearest neighbor (KNN) techniques are used for TAS, where the models were trained using 5000 samples each. It is possible to extend this work to incorporate more supervised ML/DL algorithms and larger datasets, which would improve the performance of the system. The effectiveness of these algorithms is evaluated using the element norm of \(\mathbf{H}\) and the element norm of \(\mathbf{H}^{\text{H}}\mathbf{H}\), which are two distinctive attributes of channel space. In [20], supervised ML techniques and deep neural networks (DNN) are presented and applied to address the issue of power allocation in adaptive SM-MIMO. Increasing the training set size beyond 2000 feature label pairs can improve its ABER performance. ML-based TAS methods are compared to SM without TAS (SM-WTAS) and EDAS in terms of ABER and computational complexity [21]. The SVM-based TAS scheme is less computationally complex than EDAS and does better than the classic SM with a minimum SNR gain of \(\sim\)3.4 dB.