Hybrid Deep Learning Model for Cellular Network Traffic Prediction : Case Study using Telecom Time Series Data, Satellite Imagery, and Weather Data

Abstract: Cellular network traffic prediction is a critical challenge for communication providers, which is important for use cases such as traffic steering and base station resources management. Traditional prediction methods mostly rely on historical time-series data to predict traffic load, which often fail to model the real world and capture surrounding environment conditions. In this work, we propose a multi-modal deep learning model for 4G/5G Cellular Network Traffic prediction by considering external data sources such as satellite imagery and weather data. Specifically, our proposed model consists of three components (1) temporal component (modeling correlations between traffic load values with historical data points via LSTM) (2) computer vision component (using embeddings to capture correlations between geographic regions that share similar landscape patterns using satellite imagery data and state of the art CNN models), and (3) weather component (modeling correlations between weather measurements and traffic patterns). Furthermore, we study the effects and limitations of using such contextual datasets on time series learning process. Our experiments show that such hybrid models do not always lead to better performance, and LSTM model is capable of modeling complex sequential interactions. However, there is a potential for classifying or labelling regions by their urban landscape and the network traffic.