EH model was applied. Robust beamforming and power splitting control were jointly designed for achieving the minimum transmission power and maximum EH. We transformed the non-convex minimum transmission power optimiza- tion problems into a convex form while applying a one-dimensional search algorithm to solve the maximum EH problem. Our simulation results showed that the performance achieved by using NOMA is better than that obtained by using the traditional OMA. Robust Beamforming in NOMA Cognitive Radio Networks: Gaussian CSI 6.1 Gaussian CSI Error Model In Chapter 5, we introduced a bounded channel model that defines a confined region for the channel variations. It provides a worst-case estimation. Another commonly used more realistic estimation model assumes that the channel estimation error obeys the Gaussian distribution [197, 225, 236], which is formulated as follows: = Ahk~CN(0,H). Ak E KC, (6.1a) ANEN, (6.1b) where Ahk and Agn are the channel estimation error vectors, while hk and n are the chan- nel vectors estimated at the BS side. Furthermore, Hk and Gn are the covariance matrices of the estimation error vectors. 6.2 Power Minimization-Based Problem Formulation Even though we use a different channel model, the residual interference due to imperfect CSI estimation affects the message detection similarly to the bounded error model. Thus the achievable data rate expression of SU k remains the same except that Ahk is in a new set. In contrast to the existing NOMA contributions on imperfect CSI [172], in this chapter we use the above-mentioned Gaussian estimation error model to form an optimization problem as follows [174]: (6.2a) s.t. Pr RkRk.min1-k (6.2b) (6.2c) 5G and Beyond Wireless Communication Networks, First Edition. Haijian Sun, Rose Qingyang Hu, and Yi Qian. © 2024 John Wiley & Sons Ltd. Published 2024 by John Wiley & Sons Ltd. 6 Robust Beamforming in NOMA Cognitive Radio Networks: Gaussian CSI C3 Pr (6.2d) C4 Tr (6.2e) C5 : 0<p<1, (6.2f) C6 V>0,Wk>O, (6.2g) C7 : Rank(Wf) = 1, vk e K. (6.2h) Simi