# Coordination of Macro Base Stations for 5G Network with User Clustering
###### Abstract
SEST is dedicated to the improvement of the operation strategy of communication equipment, thus optimizing the resource allocation and shutting down redundant hardware by taking advantage of the distribution characteristics of traffic loads to realize the energy reduction of the 5G network [2]. SEST at the BS level includes symbol power off, slot power off, channel power off, and PA bias-voltage adjustment [16], which improve the energy efficiency of a single BS. A further reduction of 5G network energy consumption requires the cooperation of multiple BSs in the network. At the network level, the BS sleeping method was adopted in [17], and the energy efficiency of the cell networks subject to the average connection ratio (ACR) and average user rate constraints were optimized. Numerical results show that with the sleep strategy, the energy consumption of the cell network can be reduced by approximately 21%. [PERSON] et al. designed a distributed self-adaptive (SAS) algorithm for 5G BSs networks to improve energy and reduce their carbon footprint. Each BS independently and dynamically determines its operational state. Simulation results show that the proposed algorithm significantly increases energy savings [18]. Power allocation is another promising SEST used to reduce the energy consumption of 5G networks by allocating power resources to different users while meeting the users' quality of service (QoS) [19]. Commonly used methods for determining power allocation include equal power allocation [20], the water-filling algorithm [21], branch and bound [22], and game theory [23]. [PERSON] et al. studied the joint optimization of the subchannel allocation and power allocation of 5G heterogeneous networks, and the problem was formulated as a mixed-integer nonconvex optimization problem. Simulation results showed that the proposed algorithms can attain higher system energy efficiency [24]. [PERSON] et al. investigated a cell zooming algorithm to adaptively adjust the cell size according to traffic load, user requirements, and channel conditions, greatly reducing the energy consumption and leading to green cellular networks [25; 26; 27]. Device-to-device (D2D) communication is an apt technology for proximity-based data sharing services and allows direct communication between users without a BS to reduce the transmission power consumption of BSs [28; 29]. Moreover, the design expectations for 5G networks cannot be properly articulated by a single performance objective. Some research works have proposed multi-objective optimization frameworks for the planning and optimized operation of wireless communication networks [30; 31]. However, there are two problems with the above SESTs. First, they only focus on the energy-saving problem of communication equipment in 5G BSs, while ignoring standard equipment [32] such as air conditioning (AC), backup batteries, and renewable generation units. In conventional operation strategies, backup batteries only provide an uninterruptible power supply (UPS) and are not dispatched most of the time [33]. The ACs in the cabinets are responsible for regulating the indoor temperature so that it does not exceed the upper and lower limits. However, the scheduling strategy of ACs can be improved to further reduce the energy consumption and electricity costs by making full use of the thermal inertia of the cabinets, because the indoor equipment has no comfort requirements. Second, as the number of BSs and users increases, wireless access networks become more complex, which makes it more difficult to solve the optimal dispatching scheme for 5G BSs. To tackle the aforementioned challenges, this study proposes a dispatching scheme for a 5G macro BS network incorporating the optimal scheduling of standard equipment in the BSs. The main contributions of this study are as follows. (1) A two-step energy management model for both communication equipment and standard equipment in the 5G macro BS network is proposed to reduce further the energy consumption and electricity costs.