# Power-Efficient Resource Allocation for Active STAR-RIS-Aided SWIPT Communication Systems
## 4 Simulation Analysis  
Figure 2 shows the simulation model under consideration. The AP and active STAR-RIS are placed in \((0,0,0)\) m and \((0,10,0)\) m, respectively. The direct view link from the AP to IRs and ERs is obstructed by obstacles. A semicircular area, centered on the active STAR-RIS, randomly hosts a multitude IRs and ERs situated at radii of \(d_{r}=2\) m and \(d_{t}=20\) m, respectively. The number of Monte Carlo simulations refers to the number of times the actual results are approximated through multiple experiments. As the number of Monte Carlo simulations increases, the estimated values gradually converge to the true values. The more Monte Carlo simulations there are, the closer the estimated results are to the truevalues and the higher the accuracy. The common number of Monte Carlo simulations is set to 200 [26; 27], 500 [28], 1000 [29; 30; 31], 2000 [32], and 100,000 [33]. However, although a small number of Monte Carlo simulations can obtain partially smooth curves, there are also some curves that are not smooth. Excessive Monte Carlo simulations can obtain smoother experimental results, but they consume a lot of time, while 1000 Monte Carlo simulations can obtain smoother experimental results and consume less time. Therefore, this paper sets the number of Monte Carlo simulations to 1000. For channel \(G\), the model is as follows:  
\[G=\sqrt{\frac{\rho_{0}}{d_{AR}^{A_{AR}}}}\Bigg{(}\sqrt{\frac{K_{AR}}{K_{AR}+1}} G^{LOS}+\sqrt{\frac{1}{K_{AR}+1}}G^{NLOS}\Bigg{)} \tag{40}\]  
The Rice factor is \(K_{AR}=3\) dB; \(\alpha_{AR}\) represents the corresponding path loss index. The unit path loss is \(\rho_{0}=30\) dB. \(G^{LOS}\) is deterministic line-of-sight component, \(G^{NLOS}\) is a random non-line-of-sight component, and other channels can be obtained using similar methods. The other required parameter settings are as below: The quantity of ERs and IRs is \(K_{E}=2\), \(K_{I}=2\), respectively. The initialization penalty factor is \(\eta=10^{-4}\). The accuracy of equation constraints is \(\varepsilon_{1}=10^{-7}\). The convergence tolerance of the AO method is \(\varepsilon_{2}=10^{-3}\). The maximum iterations count for the algorithm is \(n_{\max}=49\). The user noise power is \(\sigma_{i}^{2}=-120\) dBm. The scaling factor based on the penalty algorithm is \(w=10\). Assuming that all information receivers have the same QoS requirements, \(\overline{R}_{i}=\overline{R}_{0}\triangleq lb(1+\overline{\gamma}_{0})\), \(\overline{\gamma}_{0}\) is the minimal value of SINR required. The energy collection requirements for all energy receivers are \(\overline{E}_{j}=\overline{E}_{0}=1\) mW. The same simulation settings in the simulation analysis are listed in Table 2.  
Figure 3 demonstrates the convergence of the method described in this paper. Among them, the quantity of intelligent reflector units is \(M=10\), and the quantity of transmitting antennas is \(N=2\). It can be observed that the iterative method in this paper guarantees the monotonicity of the minimized AP transmit power from Figure 3.  
This paper tested three scenarios: \(\text{SINR}=5\,\text{dB}\), \(\text{SINR}=8\,\text{dB}\), and \(\text{SINR}=10\,\text{dB}\). As the SINR increasing, the value to which the system converges gradually increases. This is because with the increase of SINR, higher requirements are placed on communication quality, and therefore, the required AP transmission power gradually increases. From Figure 3, it can also be seen that after two iterations, the converg  
Figure 2: System Simulation Model. reaches about 90%. After three iterations, the AP transmission power tends towards a stable value, indicating that iterative method described in this paper is efficient. Figure 4 shows how the AP transmission power changes with the change of the quantity of AP transmission antennas \(N\) under five different models, where \(M=10\), and \(\overline{\gamma}_{0}=10\,\mathrm{dB}\).