## 5 Simulation Results
The last one uses full knowledge of transmit data symbols and faultless channel estimates into account; this sets the bar for how well the proposed identifying system operates. The results demonstrate that there are no major differences between the three scenarios, especially at intermediate and high values of SNR. This is further evidence that the intended design is successful. Figure 6 illustrates the probability of improper classification of the proposed approach for various user numbers at SNR = 14 dB and six rounds. It appears that as the number of users grows, the performance of \(P_{i}\) suffers. This is due to a rise in residual multiple access interference, which, in turn, leads to a hindering of the operation of the suggested identifier. Figure 4: MSE performance of the proposed multiuser channel predictions. The system parameters are \(U=4\), \(\mathcal{N}=2048,\langle\Phi(u)\rangle=512,u=0,1,2,3,\
u=11,\chi=60\), 10 channel paths, convolutional codes of rate 0.5, interleaved sub-carrier mapping, STBC set of S1, S2, S3, S4, S5, and modulation set of 4-QAM, 16-QAM, 64-QAM, 128-QAM, 256-QAM, and 512-QAM. Figure 3: The probability of improper classification \(P_{i}\) of the proposed classifier in comparison to the works of [43; 44]. The system’s parameters are \(U=4\), \(\mathcal{N}=2048,\langle\Phi(u)\rangle=512,u=0,1,2,3\), \(\
u=11,\chi=60\); 10 channel paths; convolutional codes of rate 0.5; interleaved sub-carrier mapping; STBC set of S1, S2, S3, S4, and S5; and a modulation set of 4-QAM, 16-QAM, 64-QAM, 128-QAM, 256-QAM, and 512-QAM. It is well known that effective initiation is necessary for SAGE-based algorithms to function properly. Figure 7 illustrates the identification performance of the proposed approach as a function of the number of training symbols, \(\chi\), at SNR = 6 and 14 dB. As can be observed, the intended identifier needs 60 training symbols to converge. It is important to note that the minimal level of the training symbols needed to get reliable preliminary predictions may vary based on the specifics of the system and the links. In  
Figure 5: The identification performance at three scenarios. The system’s parameters are SNR = 14 dB, \(\mathcal{N}=2048,\langle\Phi(u)\rangle=512,u=0,1,2,3,\
u=11,\chi=60\), 10 channel paths, convolutional codes of rate 0.5, interleaved sub-carrier mapping, STBC set of S1, S2, S3, S4, S5, and modulation set of 4-QAM, 16-QAM, 64-QAM, 128-QAM, 256-QAM, and 512-QAM. Figure 6: The impact of increasing the number of users on the proposed identifier. The system’s parameters are \(U=4\), \(\mathcal{N}=2048,\langle\Phi(u)\rangle=512,u=0,1,2,3,\
u=11,\chi=60\), 10 channel paths; convolutional codes of rate 0.5; interleaved sub-carrier mapping an STBC set of S1, S2, S3, S4, S5; and a modulation set of 4-QAM, 16-QAM, 64-QAM, 128-QAM, 256-QAM, and 512-QAM. reality, the developer can use the suggested identification algorithm with various system and link specifications to find the minimum number of the training symbols that fulfills the specifications for each set of parameters. The obtained information is then placed in lookup tables for usage in the actual hardware. The developer also has the option of selecting a large (but still manageable) set of the training symbols that work well over a broad variety of system and connection settings at random.