ndomly generated estimated channel hk and gn. 2: Initialization: Iteration number q=0,p== 1, initial value of P as Pstart' steps, end value Pend' M°, and €°, loop stop criteria Mth. 3: One-dimensional Search: 4: for p= Pstart S: Pend do repeat: {Outer Loop} Solve for the optimization problem P7: {Inner Loop} if (P7 is feasible) then Obtain Wa and V°. Break from the outer loop. end if Update M9+1 and €9+1 according to (5.23), then let q=qq1 Calculate = leti=i+1,q=0 15: end for 16: Find the maximum value among all Esum, and the precoding and energy matrix. 17: Output: Use either of the methods to get the precoding vector word and Vopt 5.4.1 Complexity Analysis For the CBS power minimization problem under the bounded CSI model, P3 K(K+1) ear matrix inequality (LMI) constraints of size (M + 1) in (13) due to the higher decoding complexity. Furthermore, we have N LMI constraints of size (M + 1) in (15) and K LMI 5 Robust Beamforming in NOMA Cognitive Radio Networks: Bounded CSI constraints of size (M + 1) in (17). Additionally, in (12g), there are (K + 1) LMI constraints associated with size M, and a total of K(K+1) + 2N + K + 2 linear constraints. Thus, accord- ing to [236] and the reference therein, the total complexity becomes (5.24) where +N+K+KIKH1).,O is the big-O notation. Furthermore, we have + 1)M + K2 + 4N + 3K + 4, and T is the accuracy of iteration. For the maximum harvested energy problem, with bounded channel model, since the difference with that of power minimization problem is that a maximum of Tmax num- ber of iterations will be performed for one-dimensional search. Hence, the complexity is Tmax Com. Numerical Results In this section, we present our simulation results for characterizing the performance of the proposed robust beamforming conceived with NOMA under the bounded estimation error models. Unless otherwise stated, the parameters are chosen as in Table 5.1. 5.5.1 Power Minimization Problem Figure 5.2 shows the empirical CDFs of the minimum transmit power of the CBS for both the imperfect CSI e