###### Abstract  
Carrier aggregation (CA) is considered as a key enabling technology to provide higher rates for users in LTE/5G networks. However, the increased transmission rate is accompanied by higher energy consumption. The existing CA energy efficiency resource optimization allocation scheme in 5G does not fully consider the following two issues, namely, the impact of delayed channel state information feedback on the rationality of resource allocation and the increasing in energy consumption caused by frequent switching of component carriers (CCs) by narrowband users; this paper proposed a CA energy-efficient joint resource optimization allocation (PEJA) scheme based on channel information prediction. The proposed scheme (PEJA) fully considers the above two issues. Firstly, the algorithm of random forest predicting channel state information is designed. On the basis, the CA energy-efficient joint resource optimization allocation (PEJA) scheme based on channel information prediction is proposed. The simulation results show that the proposed algorithm PEJA has a higher energy efficiency and throughput than the comparison algorithm under different numbers of users and different transmission powers. The PEJA algorithm is more energy efficient than the PEJA-NC algorithm, which does not consider the CC handover of narrowband users. To sum up, the proposed PEJA energy-efficient resource allocation scheme maximizes system energy efficiency and achieves a higher throughput. sustainable; random forest; resource allocation; carrier aggregation; machine learning 2022  
47-1409 gduf.edu.cn  
However, without a scientific and reasonable resource management scheme, CA technology will increase the energy consumption of base stations and users [5]. Recently, many works have been devoted to the energy-efficient resource optimization in wireless communication networks [6; 7], such as orthogonal frequency division multiple access (OFDMA) systems [8], cooperative multipoint (COMP) systems [9], cooperative relay networks [10], non-orthogonal multiple access (NOMA)-based satellite terrestrial integrated network [11], multi-user multiple input multiple output (MIMO) systems [12], and Rate-Splitting Multiple Access in Satellite and Aerial-Integrated Network [13]. The energy-efficient optimization with CA technology has also attracted much attention. The main research is as follows: paper [14] proposed to allocate resources for users on a small subset of component carriers to reduce energy consumption in LTE-advanced. Paper [15] proposed algorithms based on cross entropy to effectively solve the problem of resource allocation to maximize energy efficiency. The allocated radio resources include the user association between equipment and base station (BS), CC configuration, resource block (RB) allocation, and power allocation under QoS constraints. In addition, the sleep strategy is adopted for the BS with low traffic, which further improves the system energy efficiency. Paper [16] studied the CA energy-efficient resource allocation problem in cognitive radio systems and proposed a sub-optimal resource allocation algorithm considering energy efficiency, fairness, and system capacity. The above studies focus on the optimal allocation with CA energy-efficient resources but ignore the choice of modulation and coding scheme (MCS). Paper [17] studied the energy-efficient resource allocation for RB, CC, and MCS considering the downlink in LTE-advanced system with CA technology and adopted a proportional fairness energy scheduling strategy. However, this study does not consider power allocation. In [18], considering the downlink transmission system in LTE-advanced networks, the problem of maximizing system energy efficiency with QoS was studied and a joint resource allocation algorithm considering RB, power, and MCS was proposed. In [19], [PERSON] et al. solved the problem of RB and power allocation in LTE networks using fractional planning to maximize energy efficiency but ignored the selection of MCS and the CA capability of users.