Detecting Tendency Behavior of Cellular Network Traffic via Ordinal Pattern Transition Networks

Cellular network traffic analysis can facilitate automated decision-making as a key enabler in the future of intelligent communication systems. The existing methods mainly devote to the temporal or spatial feature acquisitions of network traffic. However, it is difficult to effectively capture the corresponding multidimensional tendency behavior. To tackle this issue, this article utilizes ordinal pattern (OP) translation networks (OPTNs) to simulate the temporal and spatio-temporal trend characteristics of cellular network traffic, aiming to capture multidimensional latent features at both the series and image levels of cellular traffic. Overall, we have proposed an analytical framework comprising trend visualization and OPTN measures. First, the original traffic is modeled as a complex network for trend feature visualization via the OP encoding, OP translation and OPTN generation. Furthermore, we suggest that some graph theoretic metrics quantify the trend features and complexity of cellular traffic and define a trend correlation (TC) metric to obtain conclusions on traffic similarity. The comprehensive analysis and discussion indicate that this OPTN spatio-temporal traffic analysis effectively simulates the complexity, predictability, and correlation of traffic. Additionally, the traffic prediction demonstration based on OPTN feature support achieved a 33.79% improvement in prediction performance compared to the baseline model. These findings underscore the potential of OPTN analysis in enhancing communication automation, including fault detection, predictability assessment, and predictive modeling guidance.