## 2 Related Studies  
With the development of high-data-rate services, the energy consumption issue attracts increasing attention. In 5G networks, more BSs have been installed to provide high-quality services to the large number of UEs. To improve energy efficiency in densely deployed BS environments, energy-efficient optimization algorithms are proposed [3; 4]. In [3], an optimization framework is developed in mmWave-based ultradense networks. In ultradense networks, most users tend to associate with MBS because MBS services higher data capacity. So it proposes a load-aware user association to redistribute the concentrated users. In order to update transmit power and associate users in an optimal way, an iterative gradient method is proposed. In [4], a subchannel and power allocation algorithm is developed for combined HetNet with a visible light communication (VLC) system. It transforms the energy-efficient resource allocation problem into a convex optimization problem and solves the problem with an alternative direction method of multipliers. BS switching operation is a popular approach to overcome the energy efficiency and consumption problems. In order to overcome the problem with guaranteed quality-of-service (QoS) requirements, various approaches have been considered in recent studies. For HetNet, many studies have been performed to overcome the energy consumption problem [5; 6; 7]. In [5], a decentralized sleep scheme with user association and resource allocation is proposed to minimize energy consumption. It solves the problem with a modified matching algorithm and voting-based algorithm. After a signal-to-interference-plus-noise-ratio (SINR)-based user association, small BSs vote for another BS that has the lowest traffic load around it. The proposed algorithm selects a small BS that has the lowest ratio of served users to the vote score. In [6], a QoS-aware topology management with transfer learning is developed. It uses QoS information from spectrum assignment as knowledge,with management transfer from the spectrum assignment knowledge to user association. A BS switching strategy with a clustering technique has been reported in [7]. It decomposes the problem into user association, BS clustering, and BS switching operation, with BS clustering into several groups and switching-operation control by clusters. C-RAN has a coexisting structure consisting of a centralized BBU pool and distributed RRHs, providing efficient resource allocation and data processing. In order to address the energy consumption problem, RRH activation strategies have been proposed for optimization using the Lagrangian theory [8; 9; 10]. In [8], an RRH activation algorithm considering resource allocation is implemented. It considers and minimizes outage probability, which is the rate of users who do not receive the required minimum. In [9], a joint RRH activation and beamforming algorithm is proposed, transferring the nonconvex problem to a concave-convex fractional program using a weighted minimum mean-square-error method. It employs the mixed \(n_{1}/n_{p}\) norm in sparse beamforming. In [10], an RRH activation algorithm considering data requirements of users is proposed. It formulates the user association as a knapsack problem to satisfy user requirements. It then organizes an active RRH set and deactivates RRHs that are not a part of the active RRH set. H-CRAN is another future architecture combining HetNet and C-RAN. In [11], a joint resource assignment and power allocation algorithm design is proposed. In order to maximize the energy efficiency, an enhanced soft fractional frequency reuse is used to characterize user association. It formulates the resource assignment and power allocation as a nonconvex function derived with the Lagrange dual-decomposition method. In [12], a joint RRH switching algorithm design with user association and resource allocation is proposed. It formulates the problem considering a limited fronthaul capacity.