## 1 Introduction  
The sixth-generation (6G) mobile communication system will be launched to fulfill unprecedented spectral efficiency (SE), energy efficiency (EE), massive connectivity, and latency demands that previous generations of networks could not meet [1, 2, 3]. These include a rise in connection density to 10 million devices per square kilometer, a doubling of SE to 100 \(\mathrm{b/s/Hz}\), and a notable decrease in latency to 0.1 ms. In the past few years, several cutting-edge wireless communication technologies have been proposed, including index modulation (IM), intelligent reflecting surfaces (IRS), cognitive radio, multi-carrier modulation, and next-generation multiple access (NGMA). These technologies will eventually enable an increase in SE, improving quality of service (QoS), regardless of the wireless system used [4]. Wireless power transfer and simultaneous wireless information and power transfer are other effective ways to deliver energy to wireless receivers [5]. Cooperative and distributed multi-point transmissions can reduce the power requirement for dense cell-based communications. On the other hand, at the expense of increased latencies, relays and multi-hop transmissions can reduce power for long-distance communications. IRS configurations are presented and implemented in [6], where the results demonstrate that IRS improves the EE of the network. IM techniques can be incorporated to boost the network's EE [1; 5; 7]. Massive multiple-input multiple-output (MIMO) is one of the methods used to enhance the SE in fifth-generation (5G) systems. A multi-antenna system leads to inter-antenna synchronization (IAS) and inter-channel interference (ICI) problems [7; 8]. As the number of radio frequency (RF) chains grows, the hardware complexity of the system rises, leading to decreased EE. To address this issue, the idea of spatial modulation (SM) is presented [7]. In SM, a single antenna is operational at a time, which alleviates the burden on the RF chain. The SE of conventional SM is given as  
\[\xi_{SM}=\log_{2}(N_{t})+\log_{2}(M), \tag{1}\]  
Here, \(N_{t}\) and \(M\) are used to represent the number of transmitting antennas and the order of the modulation, respectively. SE is limited in the SM system since it grows logarithmically with the number of transmitting antennas utilized. Therefore, more transmit antennas are needed to improve SE. Future-generation networks necessitate advanced SM variants to achieve the target SE requirements [8; 9; 10; 11; 12]. Based on their operational concept, advanced SM variants can transmit the same or different symbols over many antennas. Fully generalized spatial modulation (FGSM) and fully quadrature spatial modulation (FQSM) are two novel variants of SM in which the number of antennas employed at the transmitter is altered from one to multiple or all. Thus, the theoretical SE grows linearly with transmit antennas [12]. Additionally, as SM only uses a single antenna, it cannot accomplish transmit diversity [13; 14; 15]. In order to increase the signal-to-noise ratio (SNR) gains, transmit antenna selection (TAS) techniques are employed in the SM [13; 14; 15]. The receiver chooses the transmitter antenna subsets according to the channel quality information (CQI). In recent years, deep learning (DL), a new artificial intelligence (AI) method, has demonstrated its potential benefits in multiple areas, including speech processing, natural language processing, and image processing. A wide range of DL applications are being explored in physical-layer communications, including channel estimation, signal detection, beam prediction, etc. [16]. In this work, TAS is performed on FGSM using distinct machine learning (ML) and deep learning (DL) algorithms, whose efficacy is evaluated through classification accuracy and average bit error rate (ABER). The manuscript is structured as follows: Section 2 examines related works and research gaps. Section 3 discusses the paradigms of FGSM and the Euclidean distance-based antenna selection (EDAS) scheme.