Gaussian channel estimation models, as well as NOMA VS. OMA, is highlighted. To achieve a fair comparison between the two channel estimation error models, if the covariance matrices of the channel estimation error vector Ahk and Agn under the Gaussian model are w2I and w2, respectively, then the bounded CSI radius under the worst-case scenario of Pk and Vn should be [198] (6.17) where F. represents the complementary cumulative distribution function (CCDF) of the chi-square distribution with 2M degrees of freedom. 6.4.1 Power Minimization Problem Figure 6.1 shows the empirical CDFs of the minimum transmit power of the cognitive base station (CBS) for both the imperfect CSI estimation error models. The maximum power P is set to 2 Watts. For comparison, we also include the case of OMA, since it represents the traditional access technology. Observe that in order to reduce the inter-user interference, each OMA user relies exclusively on a single time slot. Thus, a total of K time slots are required instead of a single one in our scheme. To make a fair comparison, each SU's achievable data rate should be averaged over all K time slots, which becomes interference is achieved at the cost of a lower spectral and energy efficiency. We also observe that under both channel error models, the performance of NOMA is better than that of OMA. This is because for OMA, the lower spectral efficiency makes the SU data rate requirement harder to be satisfied. Hence the CBS has to apply a higher transmission power to compensate for that, which leads to a much higher energy consumption. Figure 6.1 is generated from 1,000 independent real- izations of different channel conditions. As expected, the performance under perfect CSI is the best, since no additional power is used to compensate for the channel uncertainties. Furthermore, in both the OMA and NOMA schemes, the performance under the Gaussian CSI channel estimation is better than that under the bounded CSI channel estimations, 6.4 Numerical Results Perfect CSI NOMA Gaussian CSI erro