## 5 Simulation Results  
Monte Carlo simulations have been used to validate the efficacy of the proposed approach. The average probability of correct categorization, represented by \(P_{a}=\frac{1}{\left\langle\psi\right\rangle}\sum\limits_{\alpha\in\psi}P(\alpha |\alpha)\), has been employed as the performance metric where \(\left\langle\psi\right\rangle\) is the cardinality of \(\psi\). Here, \(P(\alpha|\alpha)\) describes the probability that a particular code, \(\alpha\), was correctly recognized while it was employed by the \(\mathcal{S}\) node. In the absence of any further instructions, the system functions with the following settings. * The smart \(\mathcal{S}\) terminal picks a STBC waveform at random from the set of waveforms specified by the transmission matrices (1a)-(1d). * The number of observable symbols is \(M=1024\). * The utilized modulation style is 64-QAM. * The channel tap is defined for each broadcast connection as a zero-mean complex-valued Gaussian unexpected quantity. * The variance of the channel values is \(\mathrm{E}\left[\left|h_{\mathcal{S}D}^{(\alpha j)}\right|^{2}\right]=1, \mathrm{E}\left[\left|h_{\mathcal{S}R}^{(\alpha)}\right|^{2}\right]=1.5\), and \(\mathrm{E}\left[\left|h_{\mathcal{R}D}\right|^{2}\right]=1.2\). Here \(q\) is the transmit antenna index, and \(i=1,2\) refers to the time-slot index. * Each simulation is repeated 2000 times. Figure 6 displays the given approach's accuracy as a function of SNR. When the signal-to-noise ratio (SNR) is increased, both of the proposed algorithms behave much better. For example, if \(\alpha=c_{0}\) is used at the source, the probability of correctly identifying the adopted STBC at the destination using both offered methods grows from 0.78 to 0.99 as SNR increases from 5 to 15 dB. There is no noticeable increase in the performance of all codes under consideration when SNR has reached 12 dB. In addition, the functionality of the two solutions that are being provided does not vary markedly in any way. This is due to the fact that both of them rely on the same information, which is based on the peaks that are potentially accessible at certain places. Figure 7 is a representation of the categorization performance that was gained by applying the proposed solutions in addition to that which were disclosed in [29; 30; 40] for comparison purposes. According to the findings, the proposed strategies perform better than those that have been previously reported. For instance, as  
Figure 6: The categorization efficacy of the proposed solution as a function of SNR. average probability identification of the offered solutions is 0.99, where Ref. [29] gives 0.52, Ref. [30] yields 0.42, and Ref. [40] accomplishes 0.35. The justification for this is that the relay connection utilized in cooperative broadcasts is discarded by the techniques outlined in [29; 30; 40]. Furthermore, Ref. [29] employs higher-order statistics that necessitate a massive portion of information to offer satisfactory achievement, Ref. [30] implies extra receive antennas, and Ref. [40] requires channel, modulation, and timing synchronization information, whereas the proposed solutions do not. Figure 8 depicts the impact of the number of gathered symbols, \(M\), on the offered solutions. Increasing \(M\) results in a significant advancement in the performance of the categorization. For instance, increasing the number of received symbols from 128 to 2048 enhances the average probability of successful recognition from 0.56 to 0.95 when the SNR is 10 dB. Low values of the gathered symbols lead to poor classification accuracy, even when the SNR values are sufficiently high. With SNR being 20 dB, the average likelihood of successful identification is limited to 0.74 when the number of received symbols is 128. According to the findings, gathering more than 1000 symbols resulted in a satisfactory categorization performance. The theoretical conclusions presented in Section 4 are validated by this observation, hence this is a good fit.