# Intelligent Transmit Antenna Selection Schemes for High-Rate Fully Generalized Spatial Modulation
## 1 Introduction  
In comparison to fifth-generation (5G) wireless communication networks, 6G networks are expected to have much higher spectral, energy, and cost efficiency, with higher data rates (in Tbps), latency reduced by a factor of ten, connection density increased by a factor of a hundred, and increased intelligence for full automation. Innovations such as index modulation (IM), reconfigurable intelligent surfaces (RISs), non-orthogonal multiple access (NOMA), and artificial intelligence (AI) will all be introduced into 6G networks to fulfill these overall objectives [1; 2; 3; 4; 5; 6]. Massive MIMO designs can increase the spectral efficiency (SE) of 6G systems because of the higher frequencies and dense networks. In 6G, technologies like SM, RIS, NOMA, orbital angular momentum, and rate splitting multiple access have the potential to increase SE [7].  
The energy efficiency (EE) of wireless networks may be enhanced by using four major approaches: (1) Resource allocation. Rather than maximizing throughput, the EE of the network is optimized by efficiently distributing resources [8]. (2) Instead of simply increasing the coverage area, the second option employs infrastructure nodes to increase the network's EE. (3) Use renewable energy sources to power communication systems. (4) Design energy-efficient hardware solutions. In a network, IM and its derivatives can also be utilized to shoot up EE [9; 10].  
Massive MIMO is an essential technique for achieving the SE requirements of 5G. When adopting a multi-antenna system, two issues arise: inter-antenna synchronization (IAS) and inter-channel interference (ICI) [11]. With more radio frequency (RF) links, the hardware complexity of the system grows, lowering EE [12]. To overcome this problem, the concept of SM has been presented [9; 10]. Because one antenna is functional at a time, the RF chain is simpler to handle in SM [9; 10; 11].  
SM's SE is given by  
\[\alpha_{SM}=\log_{2}(M)+\log_{2}(N_{tx}), \tag{1}\]  
where the number of transmit antennas and modulation order are denoted by \(N_{tx}\) and \(M\), respectively. The SE of SM is proportional to \(log_{2}(N_{tx})\). As a result, in order to enhance SE, a higher proportion of transmit antennas is required. SM cannot satisfy SE demands since only one antenna is simultaneously active [11]. High-rate SM variants are needed for future-generation networks to meet SE requirements [13]. The high-rate SM variants may transmit the same or distinct symbols at the same time across multiple antennas, depending on their operating principle. FGSM is a cutting-edge SM variant in which the transmitter uses just one or several, or all of its antennas for transmission [13]. As a result, the possible SE grows linearly with \(N_{tx}\). [PERSON] et al. briefly discussed the system architecture and functioning theory of FGSM [13].  
Transmit diversity gain could not be achieved in SM since only one antenna is fully functional at once [14; 15; 16; 17]. The integration of TAS practices into SM improves its transmit diversity. The receiver decides on transmitter antenna subsets driven by the channel quality information (CQI). In ML, machines are taught new skills so that they may execute tasks on their own using data. In terms of planning and optimizing future-generation networks, ML plays a critical role. ML may be used to solve a wide range of technical difficulties in future-generation systems, including massive MIMO, NOMA, device-to-device (D2D) networks, etc. [2; 3]. The present research performs TAS on FGSM using ML algorithms, and the effectiveness of these algorithms is evaluated using classification accuracy and ABER.