r model OMA Gaussian CSI error model NOMA bounded CSI error model OMA bounded CSI error model Minimum transmit power of the CBS (W) Figure 6.1 The empirical CDF of the minimum transmit power of the CBS under different channel conditions. CBS antenna number M = 10, PB = 2 Watts, Rmin = 1 bit/s/Hz. as bounded CSI represents the worst-case scenario. Observe that the minimum power in the OMA-bounded CSI is over 2 Watts since we only limit the power of each time slot to 2 Watts and it is very likely that the total power over K slots will beyond that limit. Figure 6.2 shows the minimum transmit power of the CBS as a function of the mini- mum required SNR of SUs, Yk,min As the SNR increases, the power increases under all CSI cases. Also, perfect CSI requires the least power, followed by NOMA relying on the Gaus- sian CSI error model, NOMA in the bounded CSI model, OMA Gaussian CSI model, and OMA-bounded CSI model. Besides, compared to OMA, the CBS power in NOMA grows more 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 6.3a, where the perfor- mance with different CBS antenna numbers and channel uncertainties is plotted. Specifi- cally, Figure 6.3a illustrates how the number of antennas affects the overall performance. The power required increases, when the SNR of SUs grows, regardless of how many anten- nas 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). Besides, we also notice that the performance under the Gaussian error model is better than that under the bounded channel error case. In Figure 6.4b, the impact of channel uncertainties is illustrated. We set 42 = Q2 = [0.01 : 0.05], the corresponding covariance matrices 