Self-Similarity of Traffic Within a 5G Standalone Network

Research has shown network traffic to be self-similar for various connection protocols, and 5G traffic has been modeled as self-similar based on the assumption that it adheres to these previously established traffic behaviors. However, to support the continued use of known self-similar traffic models in 5G technology, it is necessary to demonstrate self-similarity within 5G network traffic in a physical environment and investigate the factors that affect it. This research uses an AMARI Callbox Mini to develop a 5G Standalone (SA) network testbed to generate and analyze 5G network traffic. Using the Rescaled Range estimation method, the Hurst parameter of this traffic is measured to determine its degree of self-similarity. Our analysis demonstrates that traffic within a 5G standalone network is statistically self-similar, and the degree to which it displays this property increases with traffic load. Furthermore, this research shows that self-similarity varies dependent on the medium in which the traffic is collected and analyzed.