# Computational-Intelligence-Based Spectrum-Sharing Scheme for NOMA-Based Cognitive Radio Networks
## 4 Simulation Results and Discussion
PSO approaches ABC at several points, but classic GA significantly lags behind in performance to reach the global best. This is due to the weak exploitation capabilities of GA despite having the best exploration capabilities, due to which classic GA frequently gets stuck in the local optima. Thus, it can be concluded that the proposed EABC-based scheme offers the best performance while saving total transmit power. Figure 3: Convergence Behavior of EABC as compared with ABC, PSO, and GA. Figure 5 shows the total power utilized by the proposed scheme, varying the transmit power of the secondary NOMA relay for \(\gamma_{0}=5\) bit/s/Hz, \(\delta=0.8\), and K = 5. Initially, for a low \(P_{5}\) high remaining transmit power is required to meet the rate requirements of SUs. However, \(P_{total}\) required by the system consistently and significantly decreases with an increase in \(P_{5}\), since SR optimizes transmit power to accommodate all users while satisfying the constraints in (11), which in turn reduces the power required by the SUs to retransmit the PU's signal. Finally, the EABC-based scheme demonstrates superior performance compared to other CI-based schemes, with a smooth transition as \(P_{5}\) is relaxed. In order to study the impact of varying the number of SUs on the transmit power of the system, Figure 6 plots \(P_{total}\) against the number of available SUs, keeping \(P_{5}=20\) mW, \(\gamma_{0}=5\) bit/s/Hz. As expected, \(P_{total}\) decreases as the number of SUs increases. This is because a cognitive network having a significantly large number of candidate SUs increases  
Figure 4: Sum Rate of SUs versus \(P_{5}\), \(\gamma_{0}=5\) bit/s/Hz, \(\delta=0.8\), K = 5.  
the probability of selecting a relay having the best channel condition to the PU to retransmit the primary signal with low power. If we closely observe the transmit power behavior, it depicts a significant initial decrease as the number of SUs starts increasing, due to the added flexibility of the relay selection process. However, a minor improvement occurs in total transmit power as the number of SUs crosses 15, indicating that a potential set of 10 SUs available for selection is sufficient to obtain significant performance results. Figure 7 illustrates \(P_{total}\) required by the proposed scheme, varying \(P_{S}\) for two cases, i.e., \(\gamma_{0}=4\) bit/s/Hz and \(\gamma_{0}=5\) bit/s/Hz. The NOMA relay optimizes the transmit power to accommodate the PU and each SU. It is observed that more power is required if the required transmission rate of the PU increases. Again, \(P_{total}\) significantly decreases with an increase in \(P_{S}\), since less power will be required by \(SU_{sel}\) to successfully retransmit \(x_{0}\). In addition, as compared to classic ABC, EABC offers more power-saving. Figure 6: \(P_{total}\) versus K, \(P_{S}=20\) mW, \(\gamma_{0}=5\) bit/s/Hz, \(\delta=0.8\). Figure 7: \(P_{total}\) versus \(P_{S}\) with different rate thresholds of PU, \(\delta=0.8\), K = 5. Finally, Figure 8 presents the sum rate achieved by EABC, varying the probability threshold \(\delta\). We have set \(\delta=0.3\), 0.8 and 1 for EABC; however, classic ABC randomly generates \(\delta\), and picked \(\delta=0.4\) for this simulation. The results of \(\delta=0.3\) for EABC are almost overlapping with those obtained with classic ABC, due to the minimum difference in \(\delta\) between the algorithms. However, significant difference is observed in performance as \(\delta\) rises to 0.8, since it sets a high standard for OBs to pick up solutions for fine-tuning. Finally, \(\delta=1.0\) updates the best solution, which validates the superior performance of the proposed EABC-based scheme.