# Joint Beam-Forming Optimization for Active-RIS-Assisted Internet-of-Things Networks with SWIPT
## 4 Simulation Results and Discussion
In comparison to the P-RIS-assisted algorithm, the A-RIS-assisted algorithm, employing dynamic power splitting, demonstrates gains of 36.5% and 45.2% when the base station's maximum allowable transmit power is set at 40 dBm and 45 dBm, respectively. This improvement is attributed to the A-RIS's ability to not only manipulate the phase of the reflected signal but also amplify its transmit power, overcoming the significant signal power decay resulting from dual-path loss. The P-RIS-assisted algorithm achieves gains of 48.7% and 40.3% when the base station's maximum allowable transmit power is 40 dBm and 45 dBm, respectively. This is owing to the P-RIS's capacity to achieve a high-array gain of the reflected beam by adjusting the phase of the reflected signal. Compared to traditional RIS-assisted algorithms without an RIS, the P-RIS-assisted algorithm provides gains of 48.7% and 40.3% when the base station's maximum allowable transmit power is 40 dBm and 45 dBm, respectively. Furthermore, in terms of power splitting algorithms, both under A-RIS assistance, the dynamic power splitting algorithm outperforms the average power splitting algorithm (\(\rho_{k}=0.5\)), resulting in increases of 21.7% and 23.5% in the total energy collected at the user's location when the base station's maximum allowable transmit power is set at 40 dBm and 45 dBm, respectively. This is due to the dynamic power splitting algorithm offering greater optimization flexibility, allowing more power to be split to enhance the total energy collected at the user's location, consequently leading to a higher energy harvest. Figure 5 plots the comparison curves of the total energy collected by the user for several different algorithms with different numbers of base station transmitting antennae, where RIS has 12 reflective units, i.e., N = 12. As can be seen from Figure 5, the total energy collected by the user under several algorithms increases accordingly after the increase in the base station input channel M. The main reason for this is that the transmitting beamwidth of the BS is fixed, and with the increase in the base station input channel, the transmitting beamwidth of each antenna becomes narrower, which increases the additional array gain,  
Figure 4: Relationship between the total energy collected at the user end and the maximum radiant power of the BS. and the available degrees of freedom of the system increases, and the performance improves consequently, and the total amount of energy collected at the user end under the various algorithms increases as well. Also, since the A-RIS has a feature that the P-RIS does not have, i.e., the enhancement of the transmitted power of the reflected signals, it allows for an increase in the power of the received signals at the user end, which results in more power being used for energy harvesting (EH); thus, the A-RIS-assisted energy-harvesting algorithm improves the sum of the energy harvested at the user end as compared to the P-RIS-assisted energy-harvesting algorithms. In addition to this, the use of the dynamic power partitioning algorithm is very effective in improving the total energy collected by the user; this is because the dynamic power partitioning algorithm improves the degree of freedom of the optimization so that more power can be used to improve the total energy collected at the user end. Compared to the average power splitting algorithm (\(\rho_{k}=0.5\)), the dynamic power splitting algorithm is used to improve the total energy collected by the user by 23.1% at the number of input channels M = 12. To evaluate the effect of different numbers of reflection units on the performance of each algorithm, Figure 6 shows the effect of different numbers of reflection units on the total energy collected from the user under each algorithm, where the number of input channels at the base station is M = 20.