## 4 Numerical Result  
In this paper, a millimeter wave system from 59 GHz to 60 GHz is proposed, and the corresponding channel is calculated by ray tracing technology. The setting of the actual office environment is shown in Figure 5, and (Length, Width, Height) is used to describe the size of the furniture. There are three receivers (Rx1, Rx2, Rx3) and one transmitter (Tx) in the office. We used short dipole antennas in this case. If the feed length of the transmitting antenna array is set to the same value, the harvesting powers at Rx1, Rx2, and Rx3 are \(2.75\times 10^{-9}\), \(6.22\times 10^{-9}\), and \(6.16\times 10^{-9}\), respectively. In our case, \(I_{n}\) is set to 1 and the SADDE algorithm is utilized to adjust the feed length for \(\alpha_{n}(f)\). We set the radius of the transmitting spherical antenna array a to 0.006 m, while the separation distance between antennas is about \(\frac{\lambda}{2}\) for decreasing the coupling in each antenna. The total number of the transmitting antennas is 16 for the beamforming technique. The \(SNR_{T}\) is set to 40 dB. Here, the \(SNR_{T}\) is the transmission power to the noise ratio. The Rx1 is used to transfer the information and harvest power with the power-dividing architecture. We set \(SNR_{T}\) to 37 dB at Rx1 for information decoding because the Rx1 uses 50% power-splitting for information decoding. Likewise, the \(SNR_{T}\) for harvested power at Rx1 is also 37 dB because the 50% power-splitting is 3 dB. Rx2 and Rx3 are used to harvest power with the \(SNR_{T}\) (The transmission power to the noise ratio)40 dB. In our study, there are four different objective functions, as shown in Table 1. Note that we use SADDE to find the minimum value of the objective features. The population size is set to 30 and the iteration is set to 500. \(W_{\text{C}}\), \(W_{\text{BER}}\), and \(W_{\text{HP}}\) are the weighting to adjust the capacity, BER, and  
Figure 6 shows the BER at Rx1 for different object functions. In Figure 6, we can see that the BERs for the multiple objective function and BER objective function are very low, at \(1.52\times 10^{-6}\) and \(1.29\times 10^{-6}\) at Rx1, respectively, Note that the total BER sum of all receivers (Rx1, Rx2, and Rx3) are \(1.9996\) and \(3.5583\times 10^{-6}\) for the multipath objective function and BER objective function, respectively. Thus, if we compare the BER at all receivers in this case, the BER objective function is much smaller than the multiple objective function, since the BER objective function optimizes all receivers, but the multiple objective function did not consider the BER at Rx2 and Rx3. Figure 7 shows the values of capacity at Rx1 for different object functions. The capacity at Rx1 for the multiple objective function is higher than that for the capacity objective function. The capacity sums of all receivers are \(0.2242\) and \(0.2102\) for the capacity objective function and the multiple objective function, respectively. It is found that the capacity sums of all receivers are the largest for the capacity objective function. Note that the objective function not only considers Rx1's performance but also optimizes the other objectives. As a result, the optimization of Rx1 will not always approach to the extremum, since the objective function optimizes all objectives. The Rx1's capacity for harvesting power objective is the smallest, since the Rx1 uses the power-splitting architecture for SWIPT. As a result, the optimization focuses on optimizing Rx2 and Rx3, since the contribution of total harvesting power is large for Rx2 and Rx3 compared to Rx1.