[7] studied a decoupled problem of user clustering and power allocation in NOMA systems in which the proposed user clustering approach is based on exhaustive search with a high required complexity. [PERSON] et al. [8] studied solutions for user pairing, and investigated the power allocation problem by using NOMA in cognitive radio (CR) networks. Nowadays, energy consumption for wireless communications is becoming a major social and economic issue, especially with the explosive amounts of data traffic. However, limited efforts have been devoted to the energy-efficient resource allocation problem in NOMA-enabled systems [9; 10; 11]. The authors in [9] maximized energy efficiency subject to a minimum required data rate for each user, which leads to a nonconvex fractional programming problem. Meanwhile, a power allocation solution aiming to maximize the energy efficiency under users' quality of service requirements was investigated [10]. [PERSON] et al. [11] proposed a gradient-based binary search power allocation approach for downlink NOMA systems, but it requires high complexity. NOMA was also applied to future machine-to-machine (M2M) communications in [12], and it was shown that the outage probability of the system can be improved when compared with OMA. Additionally, by jointly studying beamforming, user scheduling, and power allocation, the system performance of millimeter wave (mmWave) networks was studied [13]. On the other hand, CR (one of the promising techniques to improve SE), has been extensively investigated for decades. In it, cognitive users (CUs) can utilize the licensed spectrum bands of the primary users (PUs) as long as the interference caused by the CUs is tolerable [14; 15; 16]. [PERSON] et al. in [17] proposed three operation models (opportunistic spectrum access, spectrum sharing, and sensing-based enhanced spectrum sharing) to exploit the CR technique in practice. It is conceivable that the combination of CR with NOMA technologies is capable of further boosting the SE in wireless communication systems. Therefore, many studies on the performance of spectrum-sharing CR combined with NOMA have been analyzed [18; 19]. Along with the rapid proliferation of wireless communication applications, most battery-limited devices become useless if their battery power is depleted. As one of the remedies, energy harvesting (EH) exploits ambient energy resources to replenish batteries, such as solar energy [20], radio frequency (RF) signals [21], and both non-RF and RF energy harvesting [22], etc. Among various kinds of renewable energy resources, solar power has been considered one of the most effective energy sources for wireless devices. However, solar power density highly depends on the environment conditions, and may vary over time. Thus, it is critical to establish proper approaches to efficiently utilize harvested energy for wireless communication systems. Many early studies regarding NOMA applications have mainly focused on the downlink scenario. However, there are fewer contributions investigating uplink NOMA, where an evolved NodeB (eNB) has to receive different levels of transmitted power from all user devices using NOMA. [PERSON] et al. in [23] proposed a novel power control scheme, and the outage probability of the system was derived. Besides, the user-pairing approach was studied in many predefined power allocation schemes in NOMA communication systems [24] in which internet of things (IoT) devices first harvest energy from BS transmissions in the harvesting phase, and they then utilize the harvested energy to perform data transmissions using the NOMA technique during the transmission phase. The pricing and bandwidth allocation problem in terms of energy efficiency in heterogeneous networks was investigated in [25]. In addition, the authors in [20] proposed joint resource allocation and transmission mode selection to maximize the secrecy rate in cognitive radio networks.