## 4 Simulation Results and Discussion
Figure 4 also shows that our algorithms' energy efficiency performance is the same as the spectrum efficiency maximization scheme under a low power budget that both of them increase with the growth of the maximum transmission power. When the power budget becomes higher than the optimal power consumption, our algorithms' energy efficiency is constant while the spectrum efficiency maximization scheme's energy efficiency decreases. After the optimal power consumption level, our algorithms do not allocate extra energy to users, and thus the energy efficiency maintains the maximal value. However, to achieve higher spectrum efficiency, the spectrum efficiency maximization scheme will allocate as much energy to users as possible. With the increase of system throughput, the marginal efficiency of allocated power is declining, and more and more energy consumption is required to improve the spectrum efficiency for the same magnitude. This makes the energy efficiency of spectrum efficiency maximization scheme continuously reduce. When the power budget tends to infinity, the energy efficiency even converges to zero, resulting in tremendous energy waste. This phenomenon suggests that, under the premise of guaranteeing all users'  
Figure 5 shows the system energy efficiency performance measured in different minimum data transmission rate requirements, ranging from 0.1 Mbps to 1 Mbps. Simultaneously, other simulation parameters are the same as Table 1. It can be seen from Figure 5 that, in order to meet the minimum data transmission rate requirements of users, the energy efficiency of all schemes will decrease with the users' minimum data transmission rate increase. However, the NOMA systems equipped with the proposed user clustering and power allocation methods can always achieve higher energy efficiency than the OFDMA system. When the minimum data transmission rate requirements are too high, the OFDMA scheme can not satisfy all users' requirements while our algorithms can still work. To verify the proposed user clustering strategy and power allocation method's performance more finely, we compare our solution's energy efficiency against two approaches: location-based user clustering and equal power allocation. For the location-based user clustering strategy, users are manually clustered based on their geographical location so that the adjacent users tend to be assigned to the same subchannel. After user clustering, our Lagrangian multiplier based dynamic programming model is used to allocate power to each user. For the equal power allocation method, the user clustering process is the same as our pairing-assignment strategy. At the same time, the scheme achieves the power allocation that equal power is assigned to each cluster, which has the same idea as the algorithm proposed in [58]. Figures 6 and 7 illustrate the energy efficiency performance versus BS's maximum transmission power and users' minimum data transmission rate requirements, respectively, with the same scenario as Figures 4 and 5 using the parameters list in Table 1. In Figures 6 and 7, the systems equipped with the proposed user clustering and power allocation methods are still called NOMA. In contrast, location-based user clustering strategy and equal power allocation scheme are referred to as LUC and EPA, respectively. It can be seen from Figures 6 and 7 that, no matter what number of maximum transmission power and minimum data transmission rate requirements are used, the energy efficiency performance of our algorithms always outperforms both the location-based user clustering strategy and equal power allocation scheme. Figure 5: Energy efficiency versus minimum data transmission rate requirements of each user for NOMA systems equipped with the proposed user clustering and power allocation methods and OFDMA based systems. Under the low transmission power budget, the location-based user clustering strategy can achieve a high energy efficiency performance similar to the proposed pairing-assignment strategy.