## 5 Experiment Results and Analysis
When \({\rm{P_{T}}}\ >35\), the standardized energy efficiency curves of the PEJA algorithm, NP-EJA algorithm, and ERAA algorithm tend to be stable with the increase of the maximum transmission power. This is mainly because the power \({\rm{P_{T}}}\ =35\), the transmission power of the base station can meet the power demand of the system, and increasing the maximum transmission power of the base station will not improve the system energy efficiency. Furthermore, the system has limited the maximum power constraint, which is also the reason why the system energy efficiency will not increase when increasing the maximum transmission power of the base station. The energy efficiency of the GA algorithm increases with the increase in transmission power until \({\rm{P_{T}}}\ =25\) and the energy efficiency decreases rapidly with the increase in transmission power. The main reason is that the optimization goal of the GA algorithm is to maximize the utility function of the throughput. When the transmission power satisfies the user's needs, the energy efficiency will reduce with increasing the transmission power. As shown in Figures 6 and 7, in the case of different carrier aggregation capabilities (\({\rm{\dot{Ca}}}\)), the throughputs of the four comparison algorithms increase with the number of cell users. The PEJA algorithm obtains the highest throughput. Because the PEJA algorithm uses the predicted channel information, it can better schedule system resources, so the throughput obtained is greater than the NP-EJA algorithm, ERAA algorithm, and EGA algorithm using the delayed channel information. The throughput of the ERAA algorithm is larger than that of the NP-EJA algorithm, which does not use predicted channel information. Since the ERAA algorithm does not consider power allocation and the NP-EJA algorithm considers power allocation to increase the complexity of the algorithm, the throughput of the NP-EJA algorithm is slightly lower than that of ERAA algorithm. As shown in Figure 6 ((\({\rm{\dot{Ca}}=2}\)) and Figure 7 (\({\rm{\dot{Ca}}=3}\)), we can see that the greater the average carrier aggregation capacity of users, the greater the system throughput they will obtain. The cumulative distribution function (CDF) value represents the proportion of users less than or equal to the corresponding current EE value. In the case of the same EE, the lower the CDF function value is, the larger the user EE is. From the average energy efficiency CDF curve (\(\hat{\text{Ca}}=3\), \(\text{K}=15\)) shown in Figure 8, it can be seen that the standardized energy efficiency of 82% users in the EGA algorithm are less than 0.6, the standardized energy efficiency of 60% users in the ERRA algorithm are less than 0.6, the standardized energy efficiency of 43% users in the NP-EJA algorithm are less than 0.6, and only the standardized energy efficiency of 32% users in the proposed PEJA algorithm is less than 0.6. It can be seen that the proposed energy efficiency of PEJA algorithm is the highest. From the average energy efficiency CDF curve (\(\hat{\text{Ca}}=2\), \(\text{K}=15\)) shown in Figure 9, it can be seen that the normalized EE of 86% of the users in the EGA algorithm are less than 0.6, the normalized EE of 70% of the users in the ERRA algorithm are less than 0.6, the normalized EE of 44% of the users in the NP-EJA algorithm are less than 0.6, and only the normalized EE of 35% of the users in the proposed PEJA algorithm is less than 0.6. It can be seen that the proposed EE of the PEJA algorithm is the highest. From the statistical results shown in Figure 8 (\(\hat{\text{Ca}}=3\)) and Figure 9 (\(\hat{\text{Ca}}=2\)), we can see that the greater the average carrier aggregation capacity of users, the lower the EE they will obtain. However, compared with the EGA, ERAA, and NP-EJA algorithm, the PEJA algorithm has the highest EE. In order to verify the performance of the proposed CC allocation algorithm (DDJBNBC algorithm), the proposed PEJA algorithm and PEJA-NC algorithm are compared.