###### Abstract
In [20], [PERSON] et al. proposed an energy-efficient resource allocation algorithm using CA technology in LTE-advanced downlink transmission systems, considering joint CC selection, MCS allocation, and RB allocation. The optimal joint CC selection and RB allocation methods performed disjoint schemes due to a comprehensive view on the available resources [21; 22]. Paper [22] proposed QoS-aware joint CC and RB allocation satisfying the delay requirement of users in 5G. The above energy-efficient resource optimization allocation with CA mainly focused on the energy efficiency in the downlink. Since multi-carrier transmission needed to increase the uplink power consumption of user equipment, [PERSON] et al. proposed a new dynamic carrier aggregation (DCA) scheduling scheme in [23] to improve the energy efficiency of uplink and designed two scheduling algorithms, the longest serving the queue and the priority polling, so as to reduce the transmission power and maximize the utilization of radio resources. However, these studies did not comprehensively consider the impact of the uplink and downlink using CA technology on the system energy efficiency. In response to this problem, paper [24] studied the joint energy-efficient resource allocation between base stations and users, balanced the energy efficiency between uplink and downlink, each user, the jointly allocated CC, and power, but this study ignored the choice of MCS. At present, the research on the optimal energy-efficient resources allocation in CA still has the following shortcomings: under the condition that different users have different carrier aggregation capabilities and fairness, there is no related examples in the literature for the optimal energy-efficient resource allocation comprehensively considering joint CC, RB, power allocation, and MCS selection. Even if the energy-efficient resources allocation with MCS is considered, the selection of MCS is mostly based on receiving ideal channel state information and the influence of delayed channel state information feedback on system performance and resource scheduling rationality is not fully considered. In the CA energy-efficient resource scheduling scheme, the influences of signal overhead and energy consumption caused by frequent CC switching by narrowband users have not been considered. In view of the above issues, we can carry out the research work of this paper and the main contributions are as follows:  
1. A mathematical model for the optimal CA energy-efficient resources allocation is established, which comprehensively considers many factors, including the carrier aggregation capability of different users, the delayed channel information, and the selection of MCS, CC, RB, and power allocation. Since the established energy-efficient optimization model is a mixed-integer nonlinear programming (MINLP), the power, RB allocation sub-algorithm, and CC allocation sub-algorithm of energy efficiency optimization are designed to solve the problem iteratively. 2. Aiming at the problem that CQI with delayed feedback causes the resource allocation performance to be worse, the random forest algorithm is used to predict the SINR online to obtain the current SINR and its corresponding real-time CQI and MCS. 3. Under the assumption that both the CC allocation and the predicted channel information are known, the energy-efficiency optimized RB and power allocation algorithms (ERPA) are proposed. The ERPA algorithm transforms RB and power allocation problem into a fractional programming problem P\({}_{3}\) by introducing relaxation variables. The Dinkelbach theory and Lagrangian dual method are applied to solve P\({}_{3}\). The sufficient and necessary conditions for obtaining the maximum energy efficiency are proved and P\({}_{3}\) is converted to non-fractional subtraction P\({}_{4-1}\) and we obtain P\({}_{4-1}\) as the corollary of convex optimization. 4.