Cellular Traffic

Cellular traffic research focuses on predicting and optimizing the flow of data across mobile networks, aiming to improve resource allocation and network performance. Current research heavily utilizes machine learning, particularly graph neural networks and gradient boosting algorithms, to model complex spatiotemporal patterns in traffic data, often incorporating external sources like road metrics and population density estimates. These advancements are crucial for efficient 5G network management, enabling better quality of service, improved resource allocation, and potentially even informing urban planning and transportation systems through the analysis of mobility patterns derived from cellular data.