## 2 Related Works  
Research on the energy efficiency of C-RAN has been widely explored from different perspectives, including resource allocation in the BBU pool, function splitting, power control in upstream/downstream, etc. [PERSON] et.al. proposed an efficient energy saving approach by implementing virtual base station. Each cell will be assigned to virtualized network resources [16]. [PERSON] et al. developed a network energy consumption model for H-CRAN [17]. In addition, [PERSON] et al. analyzed the viable fully centralized LTE radio architectures [18]. [PERSON] et al. proposed a power model to dynamically adjust the active virtual machines in BBU pool [19]. The model evaluated the processed resource blocks (RBs). Latency imposed by virtualization, minimum data rate of mobile equipment, and the number of RBs have been considered in the model. Deep learning was used to model and predict the energy efficient mapping between baseband processing tasks and micro-servers [20]. An efficient BBU and RRUassociation algorithm was investigated on the basis of graph partitioning and rejoining [21]. The power consumption was significantly reduced by this algorithm. Further, two efficient schemes were proposed to improve both the power savings and survivability of the network by using optical ring network in C-RAN [22]. And novel bounds on the user rate function were proposed [23]. The system energy efficiency was maximized by optimizing the transmit powers while explicitly incorporating the capacity constraints on fronthaul. [PERSON] et al. addressed the problem of energy efficiency maximization in C-RAN under the constraints of fronthaul capacity and per-BS transmit power [24]. Machine learning approaches are also considered in various scenarios; for example, AI-driven autonomous optical network architectures were proposed [25; 26; 27; 28; 29], and the architectures provided power self-adaptive capability according to the network condition. Further, different fronthaul functional splits have been evaluated concerning the energy consumption of C-RAN [30]. In distributed antenna systems of C-RAN, considering the power constraints of the users, energy efficiency can be improved by designing the transmit precoding matrices [31]. [PERSON] et al. redesigned the signal quantization method to maximize the system throughput in orthogonal frequency division multiple access (OFDMA) based C-RAN [32]. The wireless power control mechanism was taken into consideration in the joint optimization also. [PERSON] et al. formulated a joint problem to minimize the aggregate power consumption [33]. The problem contains RRH mode selection, precoding design, and fronthaul compression. The tradeoff between queuing delay and energy efficiency was analyzed under the Lyapunov framework with power allocation and interference constraints [34]. Meanwhile, [PERSON] et al. proposed a cluster content caching architecture for C-RAN [35]. Two distributed algorithms were investigated to enhance the energy efficiency of the network and quality of service (QoS) experience of the mobile equipment under the architecture. Moreover, mobile cloud computing (MCC) technique was implemented in C-RAN [36]. A convex problem was formulated to joint energy minimization in the BBU pool with MCC. The problem took the constraints of tasks into consider (e.g., computing capacity, and transmitting power). Moreover, two compression approaches were introduced to maximize the downlink throughout of C-RAN over ergodic fading channels [37]. The compression technique was dependent on the functional split in the physical layer. The performance of the compression technique was influenced by the channel state information overhead caused by the large-scale of C-RAN. [PERSON] et al. proposed a novel bandwidth allocation algorithm and adjusted the upstream optical network unit (ONU) order [38]. The algorithm provided efficient low jitter upstream transmission in PON. Recently, machine learning techniques have been used for improving the performance of networks [39].