## 2 Related Work  
The prediction of cellular network traffic involves forecasting future cellular network traffic data through the analysis of the spatial-temporal distribution of known cellular traffic data. Over the past decade, deep learning techniques have gained widespread application in time-series prediction, including the prediction of vehicle flow and subway passenger flow [9; 10; 11; 12; 13; 14]. Incorporating deep learning into time-series prediction has significantly contributed to the advancement of cellular network traffic prediction. This approach eliminates the need for the manual design of feature standards, reducing the influence of human factors on the experimental results and enhancing both operational efficiency and prediction accuracy. As a result, we decided to utilize deep learning in cellular network traffic prediction to minimize resource consumption and enhance the prediction accuracy. During the initial stages, a considerable number of studies utilized probability statistical approaches or probability distribution methods to predict cellular traffic, including models like auto-regression moving average (ARMA) and auto-regressive integrated moving average (ARIMA) [15; 16; 17]. For instance, [PERSON], [PERSON], et al. [18] proposed a cellular network traffic prediction model that integrates simulated annealing (SA), ARIMA, and a back-propagation neural network (BPNN). This model effectively exploited the potential of extracting both linear and nonlinear patterns from historical cellular traffic data, leading to an enhanced accuracy in predictions. However, the traditional regression models rely heavily on extensive pre-existing knowledge based on queuing theory and network traffic flow theory and exhibit a limited efficacy in handling intricate uncontrolled variables. With the increasing complexity of networks, these models often yield a subpar performance in actual cellular network traffic prediction tasks, resulting in a reduced accuracy. With the ongoing progress of technology, the utilization of support vector machines (SVMs) and K-nearest neighbors (KNNs) has become widespread in the domain of cellular network traffic prediction [19; 20]. These models convert the prediction problem into a linear partition problem, reducing the artificial impact on prediction results and enhancing the prediction accuracy to a certain degree. However, the drawbacks of these models are evident due to the nonlinear and non-stationary nature of network traffic itself. In scenarios with numerous data samples or high dimensions, a substantial amount of computing resources is required, which fails to meet the demands of long-term network traffic prediction. With the progressive maturity of deep learning models, such as deep neural network (DNN), CNN, and LSTM [21; 22], they have commenced to yield favorable outcomes in cellular network traffic prediction. [PERSON] [23] employed particle swarm optimization (PSO) for optimizing the parameters of variable mode decomposition (VMD) and then reconstructing the cellular network traffic data. Subsequently, bidirectional long short-term memory network (BiLSTM) was utilized to carry out cellular traffic prediction. This approach circumvents the influence of manually configured parameters on the experimental outcomes and effectively mitigates the issue of inadequate feature extraction resulting from unidirectional learning. Nevertheless, one of the main drawbacks of these models is that they do not consider the spatial distribution characteristics of network traffic, but only focus on its time distribution in time series. Consequently, this often leads to a diminished prediction accuracy and a significant utilization of artificial resources. In recent years, there has been a growing trend in utilizing models like graph neural network (GNN) and CNN that demonstrate proficiency in extracting spatial features for the prediction of cellular network traffic [24; 25]. These models have proven to be effective in this domain. For instance, [PERSON], [PERSON], et al.