## 2 Related Work  
Mobile Cloud Computing (MCC) [10] can solve the problem of limited resources when mobile devices execute tasks. The studies include architecture (such as MAUI [11]) and offloading strategies (ThinkAir [12]). However, the disadvantages of this earlier scheme are high overhead and long backhaul delay. Therefore, mobile edge computing (MEC) technology has been widely studied as a network architecture that can overcome the above problems. However, the practical implementation of MEC technology still faces many challenges. The problem of resource allocation [13; 14] has always been the research focus of MEC, and one of the most critical technologies is computation offloading strategy. The mobile devices design task offloading models according to the partitionability of computation tasks, including binary offloading and partial offloading [15]. Furthermore, reference [16] proposed a wireless-powered MEC system in the unmanned aerial vehicle (UAV) scenario, studied the computation rate maximization problem under binary offloading and partial offloading modes respectively, and solved the proposed nonconvex problem by designing two-stage and three-stage algorithms. In [17], the researchers developed a reverse auction framework. They designed an offloading method based on position auction. In [18], they designed a distributed method based on game theory to manage the computation offloading tasks of wireless devices. Only adopting the computation offloading strategy still faces the problem of insufficient computing resources and high overhead. To solve this challenge, the studies on joint optimization of energy-saving control strategies for computation and communication resources are essential. In [19], the author designed single and multiple cloud server schemes to minimize energy consumption and latency time by jointly optimizing the computing rate, transmission power and offloading rate of smart mobile devices. Literature [20] proposed a scalable approximate dynamic programming algorithm to reduce average energy consumption. In addition, ref. [21] proposed three cases including Time Division Multiple Access (TDMA) scenario, Frequency Division Multiple Access (FDMA) amplify-and-forward (AF) mode, and scenario in decode-and-forward (DF) mode to minimize the total energy consumption in the system. In [22], orthogonal frequency division multiple access (OFDMA) and time division multiple access (TDMA) systems with infinite or limited cloud computing capabilities formulate the optimal resource allocation problem as a convex optimization problem to solve the minimization problem of the weighted sum mobile consumed energy under computation delay constraints. Furthermore, ref. [23] proposed joint resource allocation and user selection algorithm in MEC to maximize the energy efficiency of the mobile device. In addition, ref. [24] proposed an energy-saving routing scheme that combines the mobile sink and the static sink to decrease the corresponding transmission latency and significantly increase network lifetime. In [25], they proposed an algorithm of jointly allocating bandwidth and computational resources to mobile devices to minimize the energy consumption. As the basic strategy of the MEC system, in recent years, computation offloading methods for Time Division Multiple Access (TDMA) [26], Orthogonal Frequency Division Multiple Access (OFDMA) [27], and Code Division Multiple Access (CDMA) [28] are widely studied. In [29], work is done to minimize the weighted sum energy at the edge server and the users by FDMA technique with the constraints of the limits of computation, communication and caching capacities, as well as the computation latency. In [30], the energy minimization problem of the offloading system based on the OFDMA scheme in 5G heterogeneous networks is studied. This scheme jointly optimizes wireless and offloading resource allocation.