## 4 Design and Complexity of Proposed DL Architectures  
### DL-Based TAS Schemes for FGSM  
In this study, DL-based techniques are used to perform TAS in FGSM. Figure 2 shows the DL-based classification learning architecture for TAS. The primary steps followed by \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sr. No.** & **Input Bits** & **Antenna Set** & **Symbols** & **Transmit Vector** \\ \hline
1 & 0000 & 1, 2 & \(-1\) & \(\mathbf{\mathcal{X}}=[-1,-1,0,0]\) \\
2 & 0001 & 1, 2 & \(+1\) & \(\mathbf{\mathcal{X}}=[1,1,0,0]\) \\
3 & 0010 & 1, 3 & \(-1\) & \(\mathbf{\mathcal{X}}=[-1,0,-1,0]\) \\
4 & 0011 & 1, 3 & \(+1\) & \(\mathbf{\mathcal{X}}=[1,0,1,0]\) \\
5 & 0100 & 1, 4 & \(-1\) & \(\mathbf{\mathcal{X}}=[-1,0,0,-1]\) \\
6 & 0101 & 1, 4 & \(+1\) & \(\mathbf{\mathcal{X}}=[1,0,0,1]\) \\
7 & 0110 & 2, 3 & \(-1\) & \(\mathbf{\mathcal{X}}=[0,-1,-1,0]\) \\
8 & 0111 & 2, 3 & \(+1\) & \(\mathbf{\mathcal{X}}=[0,1,1,0]\) \\
9 & 1000 & 2, 4 & \(-1\) & \(\mathbf{\mathcal{X}}=[0,-1,0,-1]\) \\
10 & 1001 & 2, 4 & \(+1\) & \(\mathbf{\mathcal{X}}=[0,1,0,1]\) \\
11 & 1010 & 3, 4 & \(-1\) & \(\mathbf{\mathcal{X}}=[0,0,-1,-1]\) \\
12 & 1011 & 3, 4 & \(+1\) & \(\mathbf{\mathcal{X}}=[0,0,1,1]\) \\
13 & 1100 & 1, 2, 3 & \(-1\) & \(\mathbf{\mathcal{X}}=[-1,-1,-1,0]\) \\
14 & 1101 & 1, 2, 3 & \(+1\) & \(\mathbf{\mathcal{X}}=[1,1,1,0]\) \\
15 & 1110 & 2, 3, 4 & \(-1\) & \(\mathbf{\mathcal{X}}=[0,-1,-1,-1]\) \\
16 & 1111 & 2, 3, 4 & \(+1\) & \(\mathbf{\mathcal{X}}=[0,1,1,1]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: FGSM mapping for \(N_{t}=4\) and \(M=2\). these DL architectures are shown in Figure 3. First, the data is pre-processed using the mean normalization technique. DL-based systems require real-valued training datasets, while MIMO channel coefficients are complex in nature. From the pre-processed data, real-valued features are retrieved. Lastly, different DL techniques are used to train the model based on the specified features and class labels. The trained networks are used to make predictions about the classes based on the new data. \(N_{t}\) antennas are selected from \(N_{\text{T}}\) based on the class labels obtained from DL algorithms if CQI is present at the target device. Figure 3: Learning procedure of proposed DL architectures. Figure 2: DL-based classification learning for TAS in FGSM. An input vector is required to represent the channel coefficients since antenna combinations are classified against an input array. Following is a brief summary of the steps involved in the acquisition of datasets:  
1. Dataset generation: \(N_{c}\) random channel matrices \(\mathbf{H}_{\text{q}}\text{e}\mathcal{C}^{N_{r}\times N_{T}}\), \(q\in 1\cdots\ N_{c}\) are generated to create the dataset. \[\text{H}=[\mathbf{H}_{1},\mathbf{H}_{2},\cdots,\mathbf{H}_{N_{c}}]^{T}\] (5) \(\text{\bar{H}}\) consists of all these \(N_{c}\) records or channel matrices. 2. Feature vector extraction: This work utilized the absolute values of every element of the channel matrix as an input feature with size \(N_{\text{f}}=N_{r}\times N_{T}\). The resulting feature vector is given by: \[\mathbf{f}=\left[\left|\mathbf{h}_{1,1}^{q}\right|,\cdots,\left|\mathbf{h}_{1,N_{T}}^{q} \right|,\cdots,\left|\mathbf{h}_{j,k}^{q}\right|,\cdots,\left|\mathbf{h}_{N_{t},N_{T}}^ {q}\right|\right]^{T}\] (6) here \(h_{j,k}^{q}\) is the (j, k)th element of \(\mathbf{H}_{\text{q}}\), its absolute value is given as: \[\left|\mathbf{h}_{j,k}^{q}\right|=\sqrt{\mathfrak{R}\left(h_{j,k}^{q}\right)^{2}+ \mathfrak{I}\left(h_{j,k}^{q}\right)^{2}}\] (7) The mean normalization is performed to avoid the effect of bias [21]. \[v^{\text{q}}(\text{n})= \frac{\left(f^{\text{q}}(\text{n})-\text{mean}(f(\text{n}))\right) }{\left(\text{max}(f(\text{n}))-\text{min}(f(\text{n}))\right)}\,\ n\in 1\cdots N_{f},\ q\in 1 \cdots N_{c}\] (8)
3. Calculation of KPI: (3) is used to evaluate ED, which is considered the key performance indicator (KPI) in this study. The KPI is utilized to label the dataset. 4. Class labeling: There is a unique label associated with every antenna subset.