 rEn1-hk(e (6.13b) (6.2b), (6.2e), (6.2e), (6.2f), (6.2g), (6.2h). (6.13c) 6.4 Numerical Results Observe however that the transformation from (6.12a) to (6.13a) and (6.13b) is not exactly equivalent. The equivalent form should let However, by setting w to be a very small value, the transformation can be valid and it is also consistent with our Gaussian CSI error model. By applying the Bernstein-type Inequality I [194], (6.13b) becomes, Tr 2-(( 0, (6.14a) (6.14b) (6.14c) (6.14d) where 11k,s and 12k,s' K are slack variables. We also relax the problem by dropping the constraint that the rank of Wk must be 1, and the optimization problem becomes s.t. (6.14), (6.5), (6.10), (6.2e), (6.2f), (6.2g). (6.15a) Still, the coupling variable in (6.14) can be tackled by fixing p. A similar one-dimensional search for p, together with a two-loop algorithm, can solve P5; the detailed step will be omitted here for space considerations. 6.3.1 Complexity Analysis Similarly, under Gaussian error model, there are 3K(K+3) + 3N + 2 linear constraints, K(K+1) 2 +N+1 LMI of size M, and KIK+1 +K+N second-order cone (SoC) constraints. Thus, the complexity becomes: (6.16) where comp = For the maximum harvested energy problem, with Gaussian error model, the complexity is Tmax Com ; correspondingly, Tmax is the number of unitary search. 6.4 Numerical Results In this section, simulation results for characterizing the performance of the proposed robust beamforming were conceived with NOMA under both bounded and Gaussian esti- mation error models. Unless otherwise stated, the parameters are chosen as in Table 6.1. 6 Robust Beamforming in NOMA Cognitive Radio Networks: Gaussian CSI Table 6.1 Simulation parameters Parameters Values Number of SUs and PUs =3,N=2 Noise powers Minimum required EH power Pks=0.01 Watt Maximum tolerable interference of PUs Pn.p = -18 dBm Estimated channel gains Outage probability threshold Gaussian CSI estimation wh0.001,n0.0001[236] = Non-linear EH model Mk = mW, 0.014 [24] The performance comparison between bounded and 