### Related Works  
The purpose of this paper is to solve the joint user association and power control optimization problem with EE maximization. The following are some related works about the research. In [6; 7], the centralized and distributed power control methods were proposed to improve the spectral efficiency (SE) of HetNets while simultaneously considering the overall network energy consumption. [PERSON] et al. [8; 9] presented a centralized and distributed algorithm for user association in HetNets, respectively, aiming at SE maximization with user's demand satisfaction. [PERSON] et al. [10] formulated a joint optimization problem of power control and user association to improve the throughput of HetNets, while considering the fairness of users and the minimum QoS requirements of them. [PERSON] et al. [11; 12] considered the minimum QoS demand and fairness of users in line-of-sight and non-line-of-sight fading channel environments, respectively, and proposed the power control method in multi-cell network environment. However, all of the above studies focused on single antenna HetNets, which are no longer applicable when considering the impact of massive MIMO on HetNets, i.e., the channel hardening impact will cause the large-scale change of the ergodic rate of users and further influence the user-BS association. To fully profit from the benefits promised by massive MIMO and small cells, it is critical to investigate the user association problem, i.e., how to assign active users to the BS such that the system-wide performance can be maximized or enhanced [13]. Related studies about massive MIMO were also mostly done in isomorphic multi-cell network environment (not HetNets). Therefore, we can conclude that it is necessary and critical to solve the joint user association and power control optimization problem in massive MIMO enabled HetNets. To improve the EE, some related studies have also been done in massive MIMO enabled HetNets. With the help of stochastic geometry, [PERSON] et al. proposed the approach of modeling multi-tier HetNets [14]. Then, the cell density allocation algorithm was proposed to achieve a higher coverage region with saving power consumption [15], which provide the optimal simulation parameter setting of the cell density for this paper. After that, a joint optimization problem of power control and user association was proposed in HetNets [16]. However, the effect of massive MIMO was neglected. [PERSON] et al. [17] investigated the joint optimization problem of user association and interference management to maximize the network throughput. However, the EE of the network was not considered, which is the critical performance metric in 5G network scenarios. [PERSON] et al. [13; 18] proposed the centralized and distributed energy efficient user association algorithm in massive MIMO enabled HetNets while considering the fairness among users, respectively. However, the transmit power of each BS was not optimized, which is the main source of the system energy consumption. [PERSON] et al. [19] proposed a distributed power control method by using non-cooperative game theory, without considering the impact of user association on EE. The above works mainly focused on user association or power control with the EE maximization in HetNets or massive MIMO system. However, an energy efficient joint power control and user association optimization in massive MIMO enabled HetNets is scarce. Recently, [PERSON] et al. proposed different deployment modes for massive MIMO enabled HetNets [20] and then a joint user association and backhaul routing algorithm for EE maximization in [21]. We further solve the joint user association and power control optimization problem for massive MIMO enabled HetNets in this paper. In addition, there are also some related works about optimization models and algorithms for wireless network design. For example, [PERSON] et al.