## 5 Discussion on Simulation Results  
The proposed DL architectures have been implemented and evaluated in MATLAB R2022b using the deep network designer application. Table 5 outlines all of the important simulation settings. There is no dataset in any of the repositories for TAS-assisted FGSM. As the use of SE increases, so does the computing complexity of traditional EDAS. As a result, we planned to employ DL architectures by benchmarking the optimal EDAS method and creating datasets with it. The EDAS algorithm is repeatedly executed for each of the configurations listed in Table 6 to generate two different datasets of size 25,000. The label is provided by the EDAS antenna indices, and the inputs (features) are derived from the random channel matrix. This is a one-time process, and the computations are required during the offline training phase. The features such as \(\mathfrak{N}(h_{ij})\), \(\mathfrak{I}(h_{ij})\), \(\measuredangle(h_{ij})\), \(\abs{h_{ij}}^{2}\), and the squared element norm of \((\mathbf{H}^{\text{H}}\mathbf{H})\) are ignored due to their poor accuracy. As discussed in Section 4.1, \(\abs{h_{ij}}\) is used to train the model because of its higher accuracy. The classification accuracy of these proposed algorithms has been tested on 80% of holdouts. Because increasing the batch size reduces accuracy, we set the batch size at 32. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**DL-Based TAS** & \multirow{2}{*}{**Order of Complexity**} & **Configuration 1** & **Configuration 2** \\
**Scheme** & & \(\mathbf{N_{t}}=\mathbf{N_{r}}=\mathbf{8}\), & \(\mathbf{N_{t}}=\mathbf{N_{r}}=\mathbf{8}\), \\  & & \(\mathbf{N_{T}}=\mathbf{10}\), \(\mathbf{M}=\mathbf{4}\) & \(\mathbf{N_{T}}=\mathbf{10}\), \(\mathbf{M}=\mathbf{64}\) \\ \hline Traditional EDAS & \(\text{O}\Big{(}N_{p}2^{\xi_{FGSM}}\Big{(}2^{\xi_{FGSM}}-1\Big{)}8N_{r}\Big{)}\)[12] & O(0.753 \(\times\) 10\({}^{9}\)) & O(12.07 \(\times\) 10\({}^{9}\)) \\ FNN-based TAS & \(\text{O}(N_{0}N_{1}N_{2})\)[23] & O(4.194 \(\times\) 10\({}^{6}\)) & O(4.194 \(\times\) 10\({}^{6}\)) \\ RNN-based TAS & \(\text{O}(N_{0}N_{1}N_{2})\)[23] & O(4.194 \(\times\) 10\({}^{6}\)) & O(4.194 \(\times\) 10\({}^{6}\)) \\
1D CNN-based TAS & \(\text{O}(FS_{0}FS_{1}FS_{2})\)[23] & O(5.308 \(\times\) 10\({}^{6}\)) & O(5.308 \(\times\) 10\({}^{6}\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of several TAS techniques in terms of order of complexity. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Dataset Size (\(N_{c}\)) & 25,000 \\ Holdout ratio & 80–20\% \\ Mini batch size & 64 \\ Maximum epochs & 50 \\ Initial learning rate & 0.00012 \\ Optimizer & Adam \\ Learning drop rate factor & 0.15 \\ Target ABER & 10\({}^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: List of parameters used for system implementation. Figure 8: Training process of the proposed RNN architecture. Figure 7: Confusion matrix of the proposed FNN architecture. Figure 10: Training process of the proposed 1D CNN architecture. Figure 9: Confusion matrix of the proposed RNN architecture. In Figure 12, the ABER performance of proposed DL-based schemes is examined with WTAS, traditional EDAS, and two widely used ML algorithms, SVM and KNN, for \(N_{T}\) = 4, \(N_{t}\) = 3, \(N_{r}\) = 4, and \(M\) = 4. It is observed that, after the traditional EDAS, the proposed FNN-based TAS scheme outperforms SVM, KNN, and other DL-based systems. When compared to the FGSM-WTAS method, the FNN-based TAS scheme reduces the SNR requirement by \(\sim\)2 dB. Due to its higher classification accuracy, the ABER performance of FNN-based TAS is superior to other DL-based TAS schemes. When compared to [19], the disparity between the traditional EDAS scheme and the proposed method using SVM to achieve an ABER of \(10^{-4}\) is \(\sim\)7 dB, which is very large. In our case, the margin between the proposed FNN scheme and traditional EDAS scheme is smaller (\(\sim\)0.6 dB), indicating a significant improvement in the system's overall performance.