Wireless cellular traffic prediction is a critical issue for researchers and practitioners in the 5G/B5G field. However, it is very challenging since the wireless cellular traffic usually shows high nonlinearities and complex patterns. Most existing wireless cellular traffic prediction methods lack the abilities of modeling the dynamic spatial–temporal correlations of wireless cellular traffic data, thus cannot yield satisfactory prediction results. In order to improve the accuracy of 5G/B5G cellular network traffic prediction, an attention-based multi-component spatiotemporal cross-domain neural network model (att-MCSTCNet) is proposed, which uses Conv-LSTM or Conv-GRU for neighbor data, daily cycle data, and weekly cycle data modeling, and then assigns different weights to the three kinds of feature data through the attention layer, improves their feature extraction ability, and suppresses the feature information that interferes with the prediction time. Finally, the model is combined with timestamp feature embedding, multiple cross-domain data fusion, and jointly with other models to assist the model in traffic prediction. Experimental results show that compared with the existing models, the prediction performance of the proposed model is better. Among them, the RMSE performance of the att-MCSTCNet (Conv-LSTM) model on Sms, Call, and Internet datasets is improved by 13.70 ~ 54.96%, 10.50 ~ 28.15%, and 35.85 ~ 100.23%, respectively, compared with other existing models. The RMSE performance of the att-MCSTCNet (Conv-GRU) model on Sms, Call, and Internet datasets is about 14.56 ~ 55.82%, 12.24 ~ 29.89%, and 38.79 ~ 103.17% higher than other existing models, respectively.

无线蜂窝流量预测是 5G/B5G 领域研究人员和从业人员面临的关键问题。然而，这是非常具有挑战性的，因为无线蜂窝流量通常表现出高度非线性和复杂的模式。大多数现有的无线蜂窝流量预测方法缺乏对无线蜂窝流量数据的动态时空相关性进行建模的能力，因此无法产生令人满意的预测结果。为了提高5G/B5G蜂窝网络流量预测的准确性，提出了一种基于注意力的多分量时空跨域神经网络模型（att-MCSTCNet），它对邻居数据使用Conv-LSTM或Conv-GRU，日循环数据，和周循环数据建模，然后通过attention层给三种特征数据分配不同的权重，提高了它们的特征提取能力，抑制了干扰预测时间的特征信息。最后，该模型结合时间戳特征嵌入、多个跨域数据融合，并与其他模型共同辅助模型进行交通预测。实验结果表明，与现有模型相比，所提模型的预测性能更好。其中，att-MCSTCNet (Conv-LSTM) 模型在 Sms、Call 和 Internet 数据集上的 RMSE 性能与其他现有数据集相比分别提高了 13.70 ~ 54.96%、10.50 ~ 28.15% 和 35.85 ~ 100.23%楷模。att-MCSTCNet (Conv-GRU) 模型在 Sms、Call 和 Internet 数据集上的 RMSE 性能分别比其他现有模型高约 14.56 ~ 55.82%、12.24 ~ 29.89% 和 38.79 ~ 103.17%。