###### Abstract  
The energy consumption management of small base stations (SBSs) in wireless caching networks with dense deployment of SBSs is an urgent problem to be solved. This paper jointly optimizes the SBS state control and user association problems in caching networks to reduce network energy consumption while taking into account the average service latency of the network to ensure user experience. First, a new definition of three-state SBSs in caching networks is proposed based on their ability to keep content cache updated. Then, a relaxed threshold setting method is designed and the SBS traffic prediction is used to obtain the initial state information of SBSs in the next period. In order to eliminate the impact on the accessing users when the switched-off base station (BS) wakes up, a SBS state asynchronous switching mechanism is proposed to ensure that the users who switch to the waking SBS can carry out communication services normally, and a user association strategy is constructed with the SBS load as the optimization target. Finally, a joint optimization model of user association and SBS state control (SSC-UA) is constructed to admit and correct the initial state of SBSs to maximize the system gain and obtain the final state strategy for each SBS in the next period. The simulation results show that the proposed SSC-UA algorithm can effectively improve the energy efficiency and reduce the network service delay at the same time. c 2022  
into sleep, and the SBS sleep control determines which users can be switched to the set of turned-on SBSs. In the user association problem, in order to avoid MBS overload caused by traditional schemes, [PERSON] et al. [7] proposed a user association scheme that maximizes the total network rate. A user association rule that promotes maximum-minimum fairness among users is proposed in [8]. [PERSON] et al. [9] studied a framework for maximizing fair utility and demonstrated that the framework can achieve different tiers of SBSs between load balancing. However, none of the aforementioned studies consider the SBS energy efficiency. On the other hand, there have been a large number of related studies on SBS state control [10; 11; 12; 13; 14]. [PERSON] et al. [10] fixed some SBSs to be turned off during a certain time period according to the periodic fluctuation pattern of traffic. [PERSON] et al. [11] decided whether to shut down a SBS by comparing the SBS load with a set threshold value. [PERSON] et al. [12] proposed a stochastic SBS sleep scheme and a strategic SBS sleep scheme. [PERSON] et al. [13] investigated the SBS sleep algorithm based on the traffic prediction results and determined the sleep strategy by controlling the SBS transmit power and bandwidth allocation. [PERSON] et al. [14] investigated the joint user association and SBS state set optimization problem by an approximate algorithm. In addition, some works have used machine learning methods for SBS state control. [PERSON] et al. [15] proposed a reinforcement learning algorithm for small cells that adapts their activities subject to service delay constraint. The algorithm intelligently learns from the environment based on the co-channel interference, the cell buffer size, and the expected cell throughput in order to decide the best SBS state control policy. [PERSON] et al. [16] developed a traffic-aware dynamic BS sleep control framework, which presented a novel data-driven learning approach to determine the BS active/sleep modes while meeting lower energy consumption and satisfactory Quality of Service (QoS) requirements. [PERSON] et al. [17] proposed a deep reinforcement learning based approach to control SBS state. The key ingredient of the proposed scheme was to use a decision selection network to reduce the size of action space. Unlike traditional wireless networks, wireless caching networks need to consider the synchronization of content caches in addition to the traffic distribution changes in the region when controlling the state of SBSs.