# A Placement Method of the 5G Edge Nodes Based on the Hotspot Distribution of Mobile Users
## 1 Introduction
### Related Works  
Early base station placement of 3G and 4G only considers the coverage [6; 7; 8]; it is easy to cause communication congestion in hotspot areas. Therefore, in the 5G network era, how to make scientific placement and give reasonable locations of base stations has attracted widespread attention. [PERSON] et al. [9] proposed a machine learning framework based on user behavior to solve the placement problem of 5G small base stations and designed a hypergraph construction algorithm to solve it. [PERSON] et al. [10] proposed an indoor small base station placement strategy for commercial buildings based on power management to extend the use time of mobile devices. Considering both indoor and outdoor users, [PERSON] et al. [11] proposed a dynamic placement strategy to minimize data delivery costs and the utilization of the micro base station. [PERSON] et al. [12] proposed an optimal placement algorithm for UAV base stations based on a rotational placement strategy, which used a minimum number of stations to cover the user terminals. [PERSON] et al. [13] first proposed a terminal classification method based on the k-means algorithm and then deployed UAV base stations and ground base stations according to the classification results. [PERSON] et al. [14] used a probabilistic line-of-sight model to study the placement of a single UAV to support the traffic diversion of ground base stations. To improve communication quality and coverage in UAV-enabled systems, [PERSON] et al. [15] presented a systematic study on 3D placement in UAV-enabled communication systems and introduced the threat analysis of this placement. [PERSON] et al. [16] proposed a delay-tolerant and delay-sensitive 3D placement algorithm for UAV base stations and studied the association relationship between users and base stations. [PERSON] et al. [17] studied the optimization problem of joint placement of micro base stations and UAV base stations to minimize the average delay of the network. Obviously, the above research focuses on communication coverage. With the rapid evolution of the 5G network, application services through mobile intelligent terminals have become a hotspot in mobile computing. In this scenario, an efficient and reasonable 5G edge node deployment scheme can effectively reduce the computing cost and communication delay of mobile terminals and significantly improve investment efficiency and resource utilization of Telcos. Considering user requests and resource constraints, [PERSON] et al. [18] proposed an edge node deployment method based on the Dueling-DQN algorithm to improve the service requests and service responses. In order to reduce the overall cost of deploying edge networks, [PERSON] et al. [19] proposed an edge node placement framework (EdgeON). This framework implemented an optimization strategy for the edge node placement problem in the delay tolerance network. In order to balance the workload of the edge server and the communication delay between the client and the edge server, [PERSON] et al. [20] proposed an edge server deployment method based on the genetic algorithm. This method transformed the edge server deployment problem into a two-objective optimization problem under three constraints. In recent years, location services in autonomous driving and public security based on 5G edge computing have become a new research hotspot. [PERSON] et al. [21] proposed a 5G base station placement scheme to address the deployment problem of 5G edge nodes of telecom operators with low investment and high positioning accuracy. This scheme selected the locations of the 5G base station from the candidate sites by a given throughput-positioning ratio (TPR) so as to maximize both throughput and positioning accuracy. [PERSON] et al. [22] proposed a placement algorithm of edge servers based on the access point suitability assessment in a 5G network.