## 6 Simulation Results
### Detecting Backscatter Signals  
In this section, we conduct numerous simulations to assess how well the proposed HABC-DCNN performs in detecting backscattered signals. Our focus is on a BC system containing a single-antenna RF source and BD, and a multi-antenna reader, as described in Section 3. We use 50,000 examples as training and 2000 examples as test datasets, produced by using the data augmentation techniques in [29; 44]. Furthermore, the SNR is expressed in Equation (4), and the relative coefficient (as expressed in Equation (5)) is initialized as \(\Psi=-20\) dB. Figure 10 shows the BER performance versus SNR of the proposed signal detection scheme in this paper, compared with the other schemes. According to this figure, our HABC-DCNN method has demonstrated exceptional performance in backscattered signal detection, surpassing other state-of-the-art techniques. Our simulations show that HABC-DCNN achieves the best BER performance in the detection of backscattered signals. Moreover, our analysis indicates that increasing the SNR leads to a significant reduction in BER. This implies that HABC-DCNN is highly reliable in detecting backscattered signals even in the presence of noise. Figure 10: BER versus SNR for \(N=20\) and \(\Psi_{R}=-20\) dB [29; 32; 36]. Figure 9: The convergence trend of algorithms for the tension spring problem. According to Figure 11, it can be observed that the BER decreases as the number of RF source symbols, N, increases. This signifies that our method has a high sensitivity in detecting backscattered signals, and its performance can be further enhanced by increasing the number of RF source symbols used in the detection process. The results obtained from our simulations also confirm the effectiveness of HABC-DCNN compared with other works. In addition, Figure 12 reveals that increasing the ratio of the average channel gains between the direct link and the backscattered link, \(\Psi_{R}\), results in a decrease in BER. This highlights the ability of HABC-DCNN to effectively distinguish between the direct and backscattered links, and its sensitivity to changes in the channel gain ratios. This figure also shows that HABC-DCNN can provide more reliable and accurate backscattered signal detection compared to the other works, particularly in scenarios where the direct and backscattered links exhibit significant differences in channel gain. The simulation results in Figure 13 indicate that the BER increases as the distance between the BD and the reader, \(d_{BR}\), increases. This analysis reveals the limitations of conventional backscattering methods in long-range sensing applications, as the signal power decreases with increasing distance, resulting in a degraded BER. However, HABC-DCNN shows promise in addressing this limitation, as it can effectively learn and capture the underlying features of the backscattered signal, enabling it to provide more accurate and reliable detection even at greater distances. This section also assesses the performance of the HABC-DCNN and other architectures for detecting backscatter signals. The evaluation involves conducting sensitivity, accuracy, and specificity analyses, which are based on the confusion matrix and can be computed using Equations (27)-(29). \[Sensitivity=\frac{TP}{TP+FN} \tag{27}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{28}\]  
\[Accuracy=\frac{TP+TN}{TP+FN+FP+TN} \tag{29}\]  
where \(TP\) = true positive, \(FN\) = false negative, \(TN\) = true negative, and \(FP\) = false positive. Table 4 displays the sensitivity, specificity, and accuracy of various evolutionary architectures designed for detecting backscatter signals. It is evident from the table that the HABC-DCNN architecture outperforms the others in terms of sensitivity, specificity, and accuracy in both the training and validation datasets. The HABC-DCNN architecture attained accuracy values of 98.32% and 99.26% in the test and training datasets, respectively. In addition, the HABC-DCNN obtained sensitivity values of 98.89% and 99.74% in the test and train datasets, respectively.