erated by the PUs grows, where a higher Pn.p allows for a larger transmis- sion power, leading to the increase of the harvested energy. Additionally, we can see that under the Gaussian channel estimation error, the performance is better than that under the 6 Robust Beamforming in NOMA Cognitive Radio Networks: Gaussian CSI bounded channel estimation error case. When the channel conditions are better, less power is required for satisfying the data rate requirements. Hence more power can be reserved for EH. This also explains that when the required SNR is low, a high EH power can be achieved. The impact of minimum SNRs required by the SUs is illustrated in Figure 6.6. The number of CBS antennas is M = 10 and the interference threshold Pn.p is set to -24 dBm. We also NOMA bounded CSI error model NOMA Gaussian CSI error model OMA bounded CSI error model OMA Gaussian CSI error model Perfect CSI Minimum required SNR of SUs Figure 6.6 Average maximum EH power VS. the minimum SNR required by the SUs, M = 10. NOMA bounded CSI error model, M = 10 NOMA Gaussian CSI error model, M = 10 NOMA bounded CSI error model, M = 15 NOMA Gaussian CSI error model, M = 15 The number of SUs Figure 6.7 Average total EH power VS. the number of SUs for Pn.p = -24 dBm, min = 1 bit/s/Hz. 6.5 Summary list the results for the OMA cases. As expected, the average maximum EH power decreases, when the required SNR increases. Similar observations show that under perfect CSI, the performance is the best, while the OMA-bounded CSI estimation scenario is the worst. Moreover, we can see that the maximum EH power decreases significantly when the SNR grows. This is because more power has to be used for information detection, which leaves less power for energy harvesting. Figure 6.7 shows the average total EH power VS. the number of SUs. It can be observed that the total EH power grows, when the number of SUs increases, since more nodes participate in the harvesting process. Additionally, we can see that when the number of antennas is higher, more EH power 