in Gaussian CSI estimation error scenario also change according to (6.17). Clearly, channel estimation error affects the bounded CSI scenario the most, since under worst-case CSI, the channel estimation error channel becomes worse; thus, it needs more power to meet the data rate constraints. Nevertheless, the channel 6 Robust Beamforming in NOMA Cognitive Radio Networks: Gaussian CSI OMA bounded CSI error model OMA Gaussian CSI error model NOMA bounded CSI error model NOMA Gaussian CSI error model Perfect CSI Minimum required SNR of SUs Figure 6.2 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 Gaussian CSI error model, M = 10 NOMA bounded CSI error model, M = 15 NOMA Gaussian CSI error model, M = 15 Minimum required SNR of SUs Figure 6.3 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 B = 8 Watts. estimation error does not have much impact on the Gaussian channel estimation error scenario. 6.4.2 Energy Harvesting Maximization Problem In this section, we present simulation results where maximum EH is the objective function. The CBS power is PB = 2 Watts, other parameters are the same as in Table 5.1. Figure 6.5 6.4 Numerical Results NOMA bounded CSI error model NOMA Gaussian CSI error model 0.015 0.025 0.035 0.045 Channel uncertainties Figure 6.4 Impact of channel uncertainties W and Pk on the overall minimum transmit power of the CBS, M = 15, Rmin = 1 bit/s/Hz, PB = 8 Watts. Gaussian CSI error model, SNR = 0.5 Bounded CSI error model, SNR = 0.5 Gaussian CSI error model, SNR = 1.5 Bounded CSI error model, SNR = 1.5 Maximum tolerable interference of PUs (dB) Figure 6.5 Average maximum EH power under different interferences tolerated by the PUs, M = 10. characterizes the average maximum EH power VS. the interference tolerated by the to PUs. One can observe that the energy harvested monotonically increases, when the maximum interference tol