## 1 Introduction
### Research Objectives and Problem Statement  
The MEC standard was first proposed by the European Telecommunications Standards Institute (ETSI) in 2014. MEC is a better solution for computation-intensive applications in mobile networks because it can reduce the pressure of user devices and improve the performance of the network. The MEC server is the core of MEC. Users only need to upload their requirements and wait for the server to send the result back. This pattern of computing enhances the ability of user devices to run applications. It is also more secure and faster than cloud computing [6]. In this way, apps can be processed with a lower delay and energy consumption, thus improving network performance [7]. As a popular topic in current mobile communications research, edge computing can help in the following scenarios: firstly, for on-demand services on public transport, such as services where users ask for video-on-demand on airplanes or trains via their internal networks; secondly, in telesurgery, which also has high requirements for communication delay and reliability--in this application, edge computing can also reduce data transmission in the network, thereby keeping sensitive data confidential, such as the real-time status of patients' bodies [8]. In addition, in some other scenarios, including the industrial Internet of Things (lioT) [9], VR/AR interactive video and live broadcasts [10], MEC can also reduce delay and improve users' experiences [11]. With MEC technology, the allocation of communication resources will significantly affect its performance [12]. The problem studied in this work is how to realize the allocation of network resources more quickly and accurately to improve the quality of the MEC network. Conceivably, this is a dynamic optimization problem. The architecture of mobile networks is diverse. Therefore, in order to make our research more targeted, we assumed a MEC network consisting of one server and users in multiple cells. In addition, the majority of tasks in the network were downlink tasks. The resource allocation capability of a MEC network will significantly affect its quality. As a mobile network with a distributed structure, there are a large number of heterogeneous resources in the network, the server locations are relatively scattered and the power consumption of devices is also constrained [13]. In addition, bandwidth is the most valuable resource in all networks. Thus, with the development of a network, the explosive growth in the amount of data also requires the network to make full use of every spectrum resource. In short, the resource allocation ability of the network affects the effectiveness of its resources and the quality of service (QoS). There are considerable differences between resource allocation and other optimization problems associated with MEC networks. In a 5G network, network fluctuation is fast and substantial [14; 15]; thus, conventional optimization methods may not help. On one hand, with the expansion in network scale, the definition domain of the objective function expands, and the complexity of the optimization problem also increases sharply. In addition, the large number of heterogeneous resources in the network also introduces challenges in resource allocation [16]. Due to the time-varying property of mobile networks, traditional optimization algorithms must update the problem and iterate again according to each different network condition and user demand. This introduces redundant computation and extra delay. There are many new 5G applications, and optimization is also more complex than before. Therefore, the performance of conventional optimization methods may be poor [17]. On the other hand, traditional algorithms cannot solve the cooperation problem of multiple sub-networks well. The bond of all network components needs to be tighter. Usually, the exchange of network information is realized through real-time data transmission.