With the continuous development of mobile communication technology and the arrival of the 5G era, the demand for mobile data traffic is increasing. Accurate traffic prediction is of great significance in meeting users' data transmission needs and optimizing network resource allocation. However, due to the uneven distribution of cellular network traffic in large-scale urban area, the current cellular traffic prediction methods based on deep learning suffer from performance degradation as well as large storage overhead. Therefore, we design a Low Overhead wireless Traffic prediction Network (LOTNet) for performance enhancement. Firstly, to improve prediction performance under the large-scale city area, the Context Embedding and Multi-Scale Spatiotemporal Expression Long Short Term Memory (CMS-LSTM) structure and the dual attention mechanism are used in the prediction module for the extraction of spatiotemporal features. Secondly, to reduce the storage overhead caused by large-scale prediction, we use the autoencoder structure for hotspot cells extracting. The experiments on real cellular traffic data proves the effectiveness of our scheme.