## 2 Related Works  
In recent years, research into traffic allocation methods has undergone a shift from static allocation approaches to predictive dynamic allocation methods. Effectively and rapidly predicting future network traffic, whether in the short term or long term, has emerged as a critical aspect of traffic planning in satellite communication networks [10]. Conventional models like autoregressive (AR), moving average (MA), autoregressive moving average (ARMA), and autoregressive integrated moving average (ARIMA) models are limited in their ability to capture only short-term traffic correlations. With the continuous integration of technologies such as neural networks and support vector machines, prediction models based on machine learning algorithms have surfaced. These encompass artificial neural networks, least squares support vector machines (LSSVM), and extreme learning machines (ELM), among others [11]. However, these algorithms have faced challenges related to the absence of temporal correlation consideration in time series data, limited prediction accuracy, and ineffective prediction of satellite network traffic [12]. Given the intensive research into machine learning techniques, recent efforts have explored the application of machine learning and deep learning algorithms for predicting satellite network traffic. [PERSON], [PERSON], [PERSON], etc., in [13; 14; 15; 16; 17] collectively addressed the challenge of network traffic prediction within satellite networks. Among these studies, with the exception of [14], the adoption of Gated Recurrent Unit (GRU) models has demonstrated superior prediction performance compared to other algorithms such as ARIMA, ARIMA, Support Vector Regression (SVR), Random Forest, and traditional recurrent neural networks (RNNs), among others. To address the issue of limited online traffic data availability, ref. [13] employs a transfer learning approach in conjunction with the GRU neural network traffic prediction algorithm, resulting in precise predictions of satellite network traffic. Additionally, ref. [15] introduces an attention mechanism to enhance the autocorrelation by combining it with the GRU neural network, thereby enhancing prediction accuracy. The Long Short-Term Memory (LSTM) model in [17] balances the influence of different input components on the output by incorporating an attention mechanism. Furthermore, in order to incorporate spatial characteristics into the analysis, ref. [16] combines GRU with Graph Convolutional Networks (GCNs), utilizing GCN for preprocessing to extract link features for subsequent training. In [18], the author presents a kernel least mean square algorithm (KLMS) with adaptive step length and adaptive kernel width, which maps the nonlinear data from low-dimensional input space to high-dimensional feature space, and the algorithm will adaptively adjust the step length and kernel width based on the instantaneous error in the iterative process, which will provide strong decision support for traffic planning and routing design in the satellite network. Additionally, in [19], wavelet transform is employed as a preprocessing step, transforming one-dimensional time series data into three-dimensional data, which facilitates feature extraction for GRU and ultimately yields superior performance compared to RNNs. As indicated in the preceding discussion, it becomes evident that GRU outperforms other algorithms when it comes to forecasting time series data, including network traffic. However, conventional approaches to traffic prediction typically treat data sampling and prediction as separate entities. Consequently, the sampling process, which frequently employs a fixed time interval, tends to overlook variations in traffic speed. This can result in computationally intensive procedures and issues related to prediction accuracy, making such methods unsuitable for deployment in the context of advanced 6G satellite networks exemplified by ASIGN.