## 1 Introduction  
In recent years, the advent of emerging network technologies such as the edge computing, the Internet of Things (IoT), and 5G communication networks has significantly enhanced people's quality of life and facilitated deeper integration of networks into various aspects of social life. In an edge computing scenario shown in Figure 1, multiple different edge devices are connected via wireless channels to the edge compute node, which is then connected to the cloud. In this process, the edge node acts as the access for edge devices, transmitting relevant data to the cloud while running multiple downstream tasks like network analysis on these data. This wave of networking is driving a rapid expansion in network size, with the number of global Wi-Fi devices exceeding 7.2 billion by the end of 2023 and projected to reach 8.9 billion by 2028, widely distributed across industrial sites, commercial offices, home access, and other scenarios; while the total number of connections is expected to reach 100 billion by 2028 [1], and IoT Analytics further predicts that 40 billion IoT devices will be connected by 2030, predominantly wireless IoT devices [2], with the total traffic generated by mobile users and IoT devices estimated to reach 850 ZB in 2021 [3]. However, this wireless trend brings new challenges to current network technology. Transmission instability caused by environmental interference. Because the signals in the wireless communication environment are transmitted in the same space, the external environment will have a significant impact on the transmission, such as shelter in the environment, rain attenuation, etc., and this problem is more obvious in high-frequency communication (such as 5G) [4; 5]. Additionally, in high-density deployment environments, the simultaneous transmission of multiple devices may lead to channel competition and collision, such as the collision between Bluetooth Low Energy and LoRaWAN in high-density scenarios [6]. Moreover, different wireless technologies (e.g., Wi-Fi, ZigBee, Bluetooth) may interfere with each other when operating in shared spectrum bands. For instance, ZigBee and Wi-Fi operate in the same 2.4 GHz band, and without proper interference mitigation techniques, signal collisions may occur [7]. Channel competition, environmental interference, and other factors in wireless communication can disrupt packet transmission, leading to high packet loss rates in edge computing. In addition, retransmission mechanisms fail to mitigate packet loss due to persistent channel errors caused by environmental dynamics, further exacerbating the negative impact on downstream network tasks. Conventional retransmission mechanisms fail to recover lost packets under persistent channel errors (e.g., rain attenuation in millimeter-wave links) [8], and in order to reduce the interference of downstream tasks, network traffic needs to be completed and reconstructed to ensure the good operation of downstream tasks in the network. In the field of data reconstruction, data imputation algorithms have proven to be effective in various scenarios, as demonstrated in previous studies [9; 10; 11; 12]. However, network traffic presents a unique challenge due to its complexity, encompassing diverse types, lengths, and structures of missing packets, which makes it impossible to reconstruct the raw traffic data. In addition, in recent years, on edge computing networks, common downstream tasks (such as network traffic information extraction, traffic classification, and network intrusion detection) have mostly been based on processed network traffic characteristics data [13; 14; 15; 16]. For the characteristics of downstream tasks, we decided to reconstruct the network traffic characteristics instead of the full packet data. Interestingly, we have observed certain similarities between network traffic and other fields, such as image data and natural language. These domains can be abstracted as vectors, where each object may have relationships with  
Figure 1: Edge computing structure.