## 5 Simulation Results  
The proposed STBC identification method was examined through the use of Monte Carlo simulations. The following settings were considered except where otherwise noted. * The number of users was \(U=4\). * The number of total subcarriers was \(\mathcal{N}=2048\). * There were \(\langle\Phi(u)\rangle=512\), \(u=0,1,2,3\) subcarriers given for every user. * There were \(v=11\) samples of cyclic prefix. * The interleaved sub-carrier method was employed. * The assigned modulation constellation \(\Omega(u)\) for each user was picked at random from a collection of 4-QAM, 16-QAM, 64-QAM, 128-QAM, 256-QAM, and 512-QAM. * Every user employs a convolutional code of rate 0.5. * Training symbols of size \(\chi=60\) were introduced to launch the identifying task. * Each link, \(\mathbf{h}^{(k,p)}\), between antenna \(p\) of user \(u\) and the BS, had 10 taps with a delay profile as indicated in [39; 54]. * Every user was randomly given a code from {S1, S2, S3, S4, and S5} with transmission matrices being as follows [33; 55]. \[\text{S1}(d_{0},\,d_{1})=\left[d_{0},\,d_{1}\right]^{\dagger}, \tag{23}\]  
\[\text{S2}(d_{0},\,d_{1})=\left[ \begin{array}{ccc}d_{0}&d_{1}\\ -d_{1}^{*}&d_{0}^{*}\end{array} \right]^{\dagger}, \tag{24}\]  
\[\text{S3}(d_{0},\,d_{1},d_{2})=\left[ \begin{array}{ccc}d_{0}&d_{1}&d_{2}\\ -d_{1}^{*}&d_{0}^{*}&0\\ d_{2}^{*}&0&-d_{0}^{*}\\ 0&-d_{2}^{*}&d_{1}^{*}\end{array} \right]^{\dagger}, \tag{25}\]\[\text{S4}(d_{0},d_{1},d_{2})=\left[ \begin{array}{cccc}d_{0}&d_{1}&\frac{d_{2}}{ \sqrt{2}}\\ -d_{1}^{*}&d_{0}^{*}&\frac{d_{2}}{\sqrt{2}}\\ \frac{d_{2}^{*}}{\sqrt{2}}&\frac{d_{2}^{*}}{\sqrt{2}}&\frac{-d_{0}-d_{0}^{*}+d_ {1}-d_{1}^{*}}{2}\\ \frac{d_{2}^{*}}{\sqrt{2}}&-\frac{d_{2}^{*}}{\sqrt{2}}&\frac{d_{1}+d_{1}^{*}+d_ {0}-d_{0}^{*}}{2}\end{array} \right]^{\dagger}, \tag{26}\]  
\[\text{S5}(d_{0},d_{1},d_{2},d_{3})=\left[ \begin{array}{cccc}d_{0}&d_{1}&d_{2 }&d_{3}\\ -d_{1}&d_{0}&-d_{3}&d_{2}\\ -d_{2}&d_{3}&d_{0}&-d_{1}\\ -d_{3}&-d_{2}&d_{1}&d_{0}\\ d_{0}^{*}&d_{1}^{*}&d_{2}^{*}&d_{3}^{*}\\ -d_{1}^{*}&d_{0}^{*}&-d_{3}^{*}&d_{2}^{*}\\ -d_{2}^{*}&d_{3}^{*}&d_{0}^{*}&-d_{1}^{*}\\ -d_{3}^{*}&-d_{2}^{*}&d_{1}^{*}&d_{0}^{*}\end{array} \right]^{\dagger}. \tag{27}\]
* The probability of improper classification \(P_{i}\) served as a merit figure for the proposed classifier and the mean-square error (MSE) was employed to evaluate the performance of channel prediction. Figure 3 provides a visual representation of the proposed algorithm's joint modulation and STBC recognition performance over a wide range of signal-to-noise ratios (SNRs) and rounds. Additionally, Figure 4 indicates the MSE performance of the offered multiuser channel estimator. According to the results, the performance improves as the number of rounds that are utilized throughout the procedure increases; however, beyond round five, there is no visible gain made. It should be noted that an unsatisfactory performance is obtained when only a small number of training symbols are used in the initial round. When the channel decoder's output is incorporated into the identification procedure, the performance strengthens tremendously. This is due to the fact that the outputs of the decoder are fed into the proposed identifier in the same manner as if they were training symbols. The performances of the methods described in [43; 44] are also displayed in Figure 3 for the sake of comparison. It has been seen that the proposed procedure is massively superior to the alternatives. This is due to the fact that the proposed strategy is optimized for use in multiuser transmissions, whereas the methods of [43; 44] are intended for use in single-user transmissions. Figure 5 depicts the performance of \(P_{i}\) in three different scenarios in six rounds. First, we take into consideration the possibility of conducting multiuser channel predictions, as offered in (19). Second, we pay attention to the application of perfect multiuser channel predictions.