## 5 Experiment Results and Analysis  
In order to further analyze the performance of the proposed CA energy-efficient Joint Resource Allocation Algorithm (PEJA) based on channel information prediction, we established a communication simulation system for 5G with carrier aggregation. The communication network structure is that a base station eNB provide communication services for k uses by N component carriers, k uses are randomly distributed, and the downlink communication system is simulated. The main simulation parameters are set as shown in Table 1.  
The standardized energy efficiency, standardized throughput, and cumulative distribution function of standardized energy efficiency are used to evaluate the performance of the proposed scheme and algorithm. Compared with the proposed algorithm, there are the greedy resource allocation (GA) algorithm without considering energy efficiency in [20], energy efficiency greedy (EGA) algorithm with the goal of maximizing energy efficiency after the modified greedy algorithm in [20], energy efficiency resource block allocation (ERAA) algorithm, and NP-EJA algorithm in [17]. The NP-EJA algorithm does not use predicted channel information on the basis of the PEJA algorithm proposed in this chapter, the measured CQI and its mapped channel state information are directly used for the joint allocation of energy efficiency resources. As shown in Figure 4, Figure 5, Figure 6, Figure 7, Figure 8, Figure 9, and Figure 10, they are the average values of 50 experiments results. Figure 5: Standardized energy efficiency varying with maximum transmit power (\(\hat{\text{Ca}}=3\text{,K}=15\)). Figure 6: System throughput varying with the number of users (\(\hat{\text{Ca}}=2\text{,K}=15\)). Figure 7: System throughput varying with the number of users (\(\hat{\text{Ca}}=3\text{,K}=15\)). Figure 8: Cumulative distribution function (CDF) of the average energy efficiency (\(\hat{\mathsf{Ca}}=3\)). Figure 10: Comparison of CC switching control energy efficiency for narrowband users. Figure 9: Cumulative distribution function (CDF) of average energy efficiency (\(\mathsf{Ca}=2\)). The following expression is used for the standardization of evaluation indicators:  
\[{\rm{x_{i}}^{\prime}}=\frac{{\rm{x_{i}}-min(X)}}{{\rm{max(X)}-min(X)}} \tag{28}\]  
where \({\rm{x_{i}}}\) is the standardized variable value; \({\rm{x_{i}}^{\prime}}\) is the variable value after standardization; \({\rm{min(X)}}\) is the minimum value of variable sequence X; \({\rm{max(X)}}\) is the maximum value of the variable sequence X. The average carrier aggregation capability of users is defined as \({\rm{\dot{Ca}}=\sum\limits_{i=1}^{K}{C_{k}/K}}\). As shown in Figure 4, as the number of users increases, the power consumed by the system increases and the standardized energy efficiency of the four algorithms decreases steadily. It is obvious that the PEJA algorithm has the highest energy efficiency and the EGA algorithm has the lowest energy efficiency. The energy efficiency curves of NP-EJA and ERAA are close to each other but lower than those of the PEJA algorithm. The main reason is that both NP-EJA and ERAA algorithms directly use the delayed CQI of user feedback for modulation and coding, which affects the system performance. Although the ERAA algorithm can achieve the minimum energy consumption, its energy efficiency performance is lower than the PEJA algorithm. As shown in Figure 5 with the maximum transmission power \({\rm{P_{T}}}\) increasing, the standardized energy efficiency of most algorithms tends to increase, among which the PEJA algorithm is the most efficient. From the standardized energy efficiency curve in Figure 5, we can obtain the following rule: the maximum transmission power \({\rm{P_{T}}}\ =35\) of the base station is the turning point of energy efficiency of the three algorithms (PEJA, NP-EJA, and ERAA). When \({\rm{P_{T}}}\ <35\), the energy efficiency curves of the three algorithms rise rapidly with the P increasing.