# A Placement Method of the 5G Edge Nodes Based on the Hotspot Distribution of Mobile Users
## 1 Introduction  
In the 5G network of various mobile terminals and Internet of Things devices, many new applications (such as unmanned driving, augmented reality and virtual reality technology, large-scale online games, etc.) have led to the explosive growth of data communication and computing tasks in mobile networks [1]. Most of these new applications are computationally intensive and delay-sensitive applications, which have higher requirements for service delay and reliability. However, the existing mobile cloud computing paradigm, which offloads computing tasks generated by mobile devices to the cloud center for processing, makes it difficult to meet the real-time requirements of mobile users. The main reason is that this model not only consumes a lot of core network bandwidth but also has a negative impact on the overall performance of the core network.  
In order to solve the problems outlined above regarding the 5G network, the European Telecommunications Standards Institute proposed the concept of mobile edge computing [2]. The basic idea of mobile edge computing is to sink computing ability and storage capacity to the edge servers of the mobile access network near the users (such as 5G base stations), with the goal of the deep integration of traditional communication networks and Internet services [3, 4]. Therefore, mobile edge computing can not only greatly reduce thecomputing delay [5] but also significantly save the core network bandwidth of the existing cloud computing architecture and improve the economic benefits of Telcos.  
Generally, each edge node is located near the base station, and users access the edge node only through the base station. Logically, the base station and the edge node are unified. Although 5G edge nodes can be connected to multiple base stations through the core network, we only consider edge nodes that are physically close to the base station. Because other edge nodes need to connect to this base station through the core network, in our research scenario, the coverage of the 5G base station is equal to the coverage of the edge node on the base station.  
Unlike previous mobile networks, the 5G network uses high-frequency and ultra-high-frequency (UHF). The two bands of 5G defined by 3 GPP are FR1 and FR2. Our work focuses on the FR2 band. The frequency range of FR2 is 24 GHz to 52 GHz. The existing literature shows that the higher the frequency is, the smaller the coverage area is. In the case of the same power as 4G base stations, 5G base stations using FR2 have smaller coverage under the same geographical location, so more stations are needed to cover the same area. At the same time, 5G base stations are equipped with edge servers supporting computing and storage. So, the cost of 5G base stations is higher.  
Obviously, a scientific and reasonable 5G base station deployment scheme can minimize investment costs and maximize service benefits for telecom operators while meeting user requirements. Therefore, considering the cost problem, the placement method of 5G base stations can no longer adopt the full coverage mode like 4G base stations but adopt the placement method in key areas with large populations and many computing requirements (such as stadiums, railway stations, and resident areas, etc.).  
By reasonably configuring 5G base stations (called edge nodes in this article) in key areas, it can not only maximize the utilization rate of 5G base stations but also reduce the load of the core network and improve the overall benefit of Telcos. This is also the purpose of our research in this paper.  
Therefore, how to find these key areas has become our basic work. Since the key areas are generally the hotspot areas where users stay, we need to first dig out the user's stay areas by analyzing the characteristics of mobile users, such as the stay areas and dataflow of users, and so on. Then, we determine the deployment location of 5G base stations according to the user's characteristics.