# Joint User Pairing, Channel Assignment and Power Allocation in NOMA based CR Systems
## 1 Introduction  
Orthogonal multiple access (OMA) techniques have proven their efficiency in the fourth generation (4G) of communication systems [1, 2]. However, the high spectral efficiency requirements of the fifth generation (5G) of communication systems call for better technologies. Recently, non-orthogonal multiple access (NOMA) [3] has come forward and been shown to outperform OMA [4]. Some common techniques of NOMA include index modulation [5, 6], code division [7], and power domain [8]. The power domain NOMA is considered to be the backbone of the 5G communication networks because of least computational complexity and excellent performance. Hence, this paper focuses on power domain type of NOMA. In NOMA, the signals of different users are transmitted simultaneously on the same channel with different power levels. Then, on reception successive interference cancellation (SIC) is employed to remove the interference where possible. In NOMA, each channel is shared by multiple users. Thus, the spectrum efficiency of NOMA systems is very high [9]. In multi user NOMA systems the complexity and delay because of SIC increases rapidly with the number of users sharing a channel. Hence, in a practical scenario, it becomes more beneficial to consider a combination of NOMA and OMA where the available spectrum is divided into multiple channels and each channel is shared by multiple users in a NOMA fashion. Similarly, the cognitiveradio (CR) also offers high spectral efficiency. The CR employs the idea of spectrum reuse to offer high spectral efficiency. In CR, the licensed primary users (PUs) and unlicensed secondary users (SUs) communicate on the same channel, simultaneously. However, the SUs are required to keep the transmission power at a low level so that the PUs would not face harmful interference [10]. Optimization of resource allocation can significantly enhance the performance of a communication systems [11]. The problem of optimizing resource allocation in CR and NOMA systems have been considered multiple times in the literature. The authors of [12] optimized power allocation to minimize the energy consumption in a NOMA system. In [13], the authors allocated power in a NOMA system to guarantee the minimum quality of service (QoS) requirement of every user. The authors of [14] proposed a technique for energy efficiency maximization in NOMA systems. In [15], the authors studied power allocation in NOMA systems to maximize the secrecy rate of communicating users. The problem of maximizing sum rate in a NOMA system was considered by [16]. However, the work considered just two user NOMA case and did not guarantee the rate requirement of the users. Considering the problem of achieving fairness in NOMA systems, the authors of [17] proposed a sequential quadratic programming based solution. For CR systems, the authors of [18] optimized power loading for maximizing the energy efficiency of the communication system. In [19], the authors optimized power allocation to enhance the security of a CR system with a secondary receiver and one ewes dropper. The authors of [12; 13; 14; 15; 16; 17] considered single channel communication that cannot be directly mapped to multi channel systems. In NOMA, the delay of SIC increases with the number of users, hence, it is not feasible for all the users in the systems to communicate on a single channel. Thus, the solutions provided in [12; 13; 14; 15; 16; 17] lack practical application. A CR system is expected to accommodate multiple SUs. However, in [18; 19], the authors considered single SU. Some works in the literature [20; 21] have also consider multi user CR systems however did not consider the optimization of power allocation. Joint optimization of power and channel allocation can further enhance the performance of the systems. The authors of [22] optimized power and channel allocation in NOMA systems to minimize the power consumption of the network. [23] considered multi user NOMA system for energy efficiency maximization.