 followed by NOMA in the bounded CSI model and OMA bounded CSI model. Besides, compared to OMA, the CBS power in NOMA grows slowly. In the parameter setting, Yk,min plays a more important role in the constraints. For Yk,min = 2 in the NOMA case, the equivalent SNR for OMA will be 26. Thus, the gap between OMA and NOMA further increases with the required SNR. The impact of the CBS antenna number is illustrated in Figure 5.4, where the perfor- mance with different CBS antenna numbers and channel uncertainties is plotted. Specifi- cally, Figure 5.4 illustrates how the number of antennas affects the overall performance. The power required increases, when the SNR of SUs grows, regardless of how many antennas are mounted at the CBS. It is also observed that the minimum power required decreases when the number of antennas increases, since a larger number of antennas result in a higher degree of freedom (DoF). Clearly, channel estimation error affects the bounded CSI scenario (Figure 5.5). 5 Robust Beamforming in NOMA Cognitive Radio Networks: Bounded CSI OMA bounded CSI error model NOMA bounded CSI error model Perfect CSI Minimum required SNR of SUs Figure 5.3 The minimum transmit power of the CBS VS. the required SNR of SUs for M = 10, PB = 8 Watts. NOMA bounded CSI error model, M = 10 NOMA bounded CSI error model, M=15 Minimum required SNR of SUs Figure 5.4 Impact of the number of CBS antennas on the minimum transmitted power required in two imperfect CSI scenarios, M = 15, Rmin = 1 bit/s/Hz, PB = 8 Watts. 5.5.2 Energy Harvesting Maximization Problem In this section, we present results for the maximum EH as our objective function. The CBS power is P = 2 Watts. Figure 5.6 characterizes the average maximum EH power VS. the interference tolerated by the PUs. One can observe that the energy harvested monotonically increases, when the maximum interference tolerated by the PUs grows, where a higher 5.5 Numerical Results 0.015 0.025 0.035 0.045 Channel uncertainties Figure 5.5 Impact of channel uncertainties Yn and Pk on the