# Joint Beam-Forming Optimization for Active-RIS-Assisted Internet-of-Things Networks with SWIPT
## 4 Simulation Results and Discussion
It can be observed from Figure 6 that there is no relationship between the total energy collected from the user and the reflection units for the traditional algorithm without RIS assistance, mainly because the RIS has been removed from the algorithm. This led to no change in the total energy captured by the user as the number of reflection units increased. With the increase in the number of reflection units, the energy collected by the user of the proposed three algorithms, namely, the energy-harvesting algorithm with dynamic power splitting assisted by A-RIS, the energy-harvesting algorithm with average power splitting (\(\rho_{k}=0.5\)) assisted by A-RIS, and the energy-harvesting algorithm assisted by P-RIS, increases and the performance of the algorithms is also enhanced. This finding also laterally verifies that configuring more reflection units can increase the reflection path from the base station to the user, further  
Figure 5: Relationship between the total energy collected at the user end and the number of antennae. expanding the multipath propagation and making the received array gain at the user end larger, and by optimizing the RIS phase shift, the received signal power at the user end is enhanced so that more power is used to boost the total energy collected at the user end. In addition, as shown in Figure 6, when using the energy-harvesting algorithm assisted by the A-RIS, the total energy collected by the user is further increased because the A-RIS amplifies the signal strength, and the total energy collected at the user end can be effectively increased compared to the use of the P-RIS. Figure 7 shows the variation curves of the energy collection performance of several different algorithms at the user end for different user-required minimum signal-to-dry-noise ratio \(\gamma_{\text{min}}\), where the number of reflector units of RIS is N = 12 and the number of base station input channels is M = 20. It can be seen from Figure 7 that with the increase in the user-required sub-minimum signal-to-dry noise ratio \(\gamma_{\text{min}}\), the user's energy collection of several algorithms decreases correspondingly, and the total energy collected decreases accordingly. The main reason for this phenomenon is that to satisfy the more stringent user minimum SINR \(\gamma_{\text{min}}\) constraint, the base station antenna (BS) needs to allocate more power to the user for information decoding (ID), which results in less power left to be allocated for energy harvesting (EH), resulting in lower energy harvested from the user. It can also be seen that due to the presence of dual path losses, the sum of energy harvested by the user by the P-RIS-assisted energy-harvesting algorithm does not differ significantly compared to the conventional RIS-assisted energy-harvesting algorithm without RIS-assisted energy harvesting. However, since the dynamic power splitting energy-harvesting algorithm with A-RIS assistance proposed herein can give full play to the advantage that A-RIS can amplify the reflected signal radiant power, it can overcome the defect of the \"multiplicative fading\" of P-RIS so that the sum of energy harvested at the user end can be improved. Further, the dynamic power partitioning algorithm increases the degree of freedom of the optimization so that more power is available for energy harvesting (EH), resulting in more energy harvested by the user. The sum of energy harvested at the user end with the dynamic power splitting algorithm is improved by 70.1% compared to the energy  
Figure 6: Relationship between the total energy collected at the user end and the number of RIS reflector units. harvesting algorithm with average power splitting (\(\rho_{k}=0.5\)) assisted by A-RIS when the minimum required signal-to-dry noise ratio \(\gamma_{\text{min}}\) = 2 dB at the user end is satisfied.