NOMA in CR compared to that achieved by using OMA in CR. On the other hand, the increasing greenhouse gas emissions have become a major con- cern also in the design of wireless communication networks. According to [81], cellular networks world-wide consume approximately 60 billion kWh energy per year. Moreover, this energy consumption is explosively increasing due to the unprecedented expansion of wireless networks to support ubiquitous coverage and connectivity. Furthermore, because of the rapid proliferation of IoT applications, most battery-driven power-limited IoT devices become useless if their battery power is depleted. Thus it is critical to use energy in an efficient way or to harness renewable energy sources. As remedy, energy harvesting (EH) exploits the pervasive frequency radio signals for replenishing the batteries [118]. There have been two research thrusts on EH using RF technology. One focuses on wirelessly pow- ered networks, where a so-called harvest-then-transmit protocol is applied [22]. The other one uses SWIPT [138-143], which is the focus of this chapter. The contributions of SWIPT in CR have been extensively studied. Specifically, authors of [186] considered the optimal beamforming design in a multiple-input single-output (MISO) CR downlink network. A similar power splitting structure to that of our work is applied at the user side. Hu et al. [79], on the other hand, investigated the objective function of EH energy maximization, and a resource allocation problem was formulated to address that goal. Additionally, Mohjazi et al. [135] considered the underlay scheme in CR network and proposed the optimal beamform- ing design. To address both the SE and EE, a MISO NOMA CR using SWIPT is considered 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. 5 Robust Beamforming in NOMA Cognitive Radio Networks: Bounded CSI based on a practical non-linear EH model. Robust beamform