ms in NOMA CR-aided SWIPT. 5.1.2 Contributions This chapter expands [22] in three major contexts. Firstly, a NOMA MISO CR-aided SWIPT is considered, while a OMA MIMO wireless powered network was used in [22]. Secondly, the work in [22] relies on the bounded CSI error model, while both the bounded and the gaussian CSI error model are applied in our work. Thirdly, we consider the minimum trans- mit power as the optimization objective, which is not considered in [22]. 5 Robust Beamforming in NOMA Cognitive Radio Networks: Bounded CSI 5.2 System and Energy Harvesting Models 5.2.1 System Model We consider a downlink CR system with one cognitive base station (CBS), one primary base station (PBS), N PUs, and K SUs [174]. The CBS is equipped with M antennas, while each user and PBS have a single antenna. It is assumed that the SUs are energy-constrained and energy harvest circuits are used. Specifically, the receiver architecture relies on a power splitting design. Once the signal is detected by the receiver, it will be divided into two parts. One part is used for information detection, while the other part for energy harvest- ing. Similar structures can be found in [110, 216]. To better utilize the radio resources, all UEs are allowed to access the same resource simultaneously. To be specific, the PBS sends messages to all PUs, while the CBS communicates with all SUs simultaneously by applying NOMA principles by controlling the interference from the CBS to PUs below a certain level [111] (Figure 5.1). Let us denote the set of SUs and PUs as K = {1,2, , K} and N = {1,2, N}, respectively. The signal received by the kth SU can be expressed as (5.1) where hk E CMx1 is the channel gain between the CBS and the kth SU, while ns is the joint effect of additive white Gaussian noise (AWGN) and interference from the PBS. ns , CN (0,0kg), where OKS is the power. This interference model represents a worst-case sce- nario [138]. Furthermore, X is the message transmitted to SUs after precoding. According to the NOMA principle, we have