## 2 Related Work  
Several studies related to WPMEC have been conducted. The European Telecommunications Standardization Institute clearly defined the concept of MEC in Ref. [6]. [PERSON] et al. [7] discussed the transition from a cloud architecture to an edge computing architecture and outlined six major challenges for future edge computing, including how UNs with limited energy can achieve low-latency data offloading.  
Wireless power transfer (WPT), particularly in the form of a wireless powered communication network (WPCN) [8; 9; 10], has been recognized as an important method for providing continuous power for mobile communications. The authors of Ref. [11] first applied a wireless energy supply to a cloud computing architecture. UNs first harvested energy and then transmitted data to the cloud. Under energy and delay constraints, an appropriate computing mode (local, or offloading to the cloud server) was selected to maximize the probability of successful computing. [PERSON] et al. [12] proposed a unified MEC-WPT design by considering a wireless powered multiuser MEC system, where an AP integrated with an MEC server was produced to broadcast wireless power to charge UNs, received offloaded data from UNs, and executed computation tasks. This system reduced the transmission energy of UNs for sending data to the cloud. Based on this system, [PERSON] et al. [13] proposed an online learning algorithm for computation offloading using a distributed execution manner in multiple-U, multiple-AP scenarios. [PERSON] et al. [14] proposed a joint DL-based framework that effectively initiates the worthy offloading solution in the orthogonal frequency-division multiple access (OFDMA) WPMEC. [PERSON] et al. [15] aimed to generate an effective offloading choice between local and remote computation in a real-time environment. However, these measures still did not resolve the \"near-far\" effect, which impacted network performance [16; 17]. In effect, UNs that are far from the AP harvest less energy but have a longer communication distance than those closer to the AP. Consequently, the \"far\" UNs require more time than the \"near\" UNs to transmit their data.  
An effective way to solve this problem is to establish cooperation between the UN and the design routing, power control, or time distribution mechanisms. The authors of Ref. [18] considered the \"near-far\" effect of the WPMEC. By employing joint cooperative transmission and the allocation of computing resources, the AP's transmit power minimization problem was effectively solved, and energy efficiency was improved. In Ref. [19], a wireless-powered non-orthogonal multiple access (NOMA)-aided MEC framework was proposed, in which joint communication and computation cooperation was considered to overcome the \"near-far\" effect. Based on this framework, they minimized energy consumption by jointly optimizing the transmission power, while satisfying the maximum tolerance delay. However, this research was conducted and the results derived only in scenarios with two UNs and cannot be applied to large-scale networks. In Ref. [13], the authors proposed an online learning algorithm for computation offloading in large-scale WPMECs using a distributed execution manner. It is assumed that the transmission power of UNs is fixed in the algorithm. This assumption cannot be applied to practical scenarios in which the power of UNs is variable. In Ref. [20], the authors minimize the total energy consumption of the system using helper node cooperation by jointly considering energy beamforming, timeslot assignment, computation-task allocation, and power allocation. This system required additional helper nodes exhibiting sufficient resources to guarantee data forwarding, which introduced extra overhead. Therefore, it is significant to identify a method for minimizing delay based on UN cooperation that is suitable for multiple UN scenarios.