Cellular traffic prediction with machine learning: A survey

• Published in: Expert systems with applications Vol. 201; p. 117163
• Main Author: Jiang, Weiwei
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.09.2022; Elsevier BV
• Subjects: Artificial intelligence; Cellular communication; Cellular network; Clustering; Communications systems; Decomposition; Deep learning; Machine learning; Prediction models; Traffic prediction; Traffic surveys; Deep learning; Traffic prediction; Decomposition; Clustering; Cellular network; Machine learning
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2022.117163

Cellular networks are important for the success of modern communication systems, which support billions of mobile users and devices. Powered by artificial intelligence techniques, cellular networks are becoming increasingly smarter, and cellular traffic prediction is an important basis for realizing various applications that have originated from this trend. In this survey, we review the relevant studies on cellular traffic prediction and classify the prediction problems as the temporal and spatiotemporal prediction problems. The prediction models with artificial intelligence are categorized into statistical, machine learning, and deep learning models and then compared. Various applications based on cellular traffic prediction are summarized along with their current progress. The potential research directions are pointed out for future research. To the best of our knowledge, this paper is the first comprehensive survey on cellular traffic prediction. •Artificial intelligence for cellular traffic prediction is reviewed comprehensively.•Cellular traffic prediction problems, models, and evaluation metrics are classified.•The potential applications and directions for future research are pointed out.