# Computational-Intelligence-Based Spectrum-Sharing Scheme for NOMA-Based Cognitive Radio Networks
## 4 Simulation Results and Discussion  
In order to prove the effectiveness of the proposed EABC-based power allocation scheme, its performance is compared with the classic ABC algorithm and with the competent GA and PSO algorithms. The system parameters are set as follows. The number of PUs is set to 1, \(\gamma_{k}=1\) bit/s/Hz, noise power is \(10^{-8}\) mW, and path loss exponent is set to 2. The coordinates for the PBS are set to (0,0), whereas those for SR and \(Q\) are set to (50,\(-10\)) and (250,10), respectively, and thus the PBS and the receiver \(Q\) are separated far apart. For the SU cluster, the horizontal coordinates are randomly generated between 100 m and 150 m. In order to ensure a fair comparison among all CI-based algorithms, the transmit power allocated by the NOMA relay to each user is optimized according to (11). For PSO, \(\alpha_{1}\) and \(\alpha_{2}\) are set to 1.5 and \([v_{min},v_{max}]=[-9,+9]\). For all algorithms, population size is J = 20 and MGN = 500. The parameter settings for GA are provided in Table 3.  
Figure 3 illustrates the convergence behavior of EABC as compared with classic ABC, PSO, and GA by observing the total transmit power of the system for a given \(P_{S}=10\) mW, \(\gamma_{0}=5\) bit/s/Hz, \(\delta=0.8\), K = 5. It can be seen clearly that EABC quickly converged to the global best in less than 15 generations, whereas ABC converged in approximately 25 generations. Both EABC and ABC converged quickly due to their simple implementation, involving fewer control parameters per generation, which in turn reduces the hardware burden. \begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Values** \\ \hline Selection & Stochastic uniform \\ Mutation & Adaptive feasible \\ Crossover & Heuristic \\ Crossover fraction & 0.2 \\ Function tolerance & \(1\times 10^{-15}\) \\ Migration direction & Both ways \\ Scaling function & Rank \\ \hline \end{tabular}
\end{table} 
Table 3: Parameter Settings for GA. Furthermore, EABC showed superior convergence performance to the classic ABC algorithm due to the high value of probability threshold \(\delta\) set for fine-tuning of solutions, i.e., \(\delta=0.8\). This smart behavior of updating the finest solutions not only improves the solution quality but also increases the speed of convergence. In contrast, PSO and GA converged in approximately 40 and 120 generations, respectively. The convergence performance of different global optimizers varies significantly depending upon the optimization parameters and the nature of the operations involved. In ABC and PSO, only the local and global best share fitness information with other candidate solutions. However, GA took the longest convergence time because the information is shared among all chromosomes in GA, and in addition, several operations and parameters are involved in every generation, which affects the settling time of the algorithm, thus requiring more generations to reach the global optima. Figure 4 illustrates the sum rate of SUs varying the transmit power \(P_{S}\) at the secondary NOMA relay. The required transmission rate of PU is set to \(\gamma_{0}=5\) bit/s/Hz for reference. The results show that the sum rate of SUs increases as \(P_{S}\) is relaxed; however, the required threshold for primary network is not met at low values of \(P_{S}\), i.e., \(P_{S}<20\) mW. At the two extremes, the minimum transmit power required at the NOMA relay to satisfy the primary threshold is \(P_{S}=20\) mW for EABC, while GA achieves the same rate at \(P_{S}=32\) mW. The performance of ABC and PSO lies in between the two, with 23 mW for ABC and 27 mW for PSO. Thus, EABC reduces power consumption by 13.95% compared to ABC, while 29.78% and 46.15% more power saving is achieved compared to PSO and GA, respectively. Once QoS of the primary network is met, the sum rate of SUs continues to increase with \(P_{S}\). Moreover, we can clearly observe that EABC outsmarts all the three implementations for the whole range of \(P_{S}\), keeping \(\gamma_{0}\) fixed, followed by ABC in performance.