Leveraging L-moments to characterize traffic behavior in 4G and 5G networks

Abstract

While 4G networks have served as the foundation for mobile broadband services, their architecture presents limitations in handling the increasing demand for higher data rates, lower latency, and large-scale connectivity. The transition to 5G addressed these constraints by introducing a more flexible and efficient network design, incorporating key enabling technologies such as virtualization and network slicing. These innovations enhance resource allocation, mobility support, and service differentiation, making 5G a more capable solution for high-demand applications. However, despite these advancements, understanding how traffic behavior differs between 4G and 5G remains a critical challenge, particularly in high-mobility scenarios, where fluctuations in network performance can significantly impact Quality of Service (QoS). To analyze these differences, we examine downlink (DL) bitrate, signal quality, and mobility patterns in both technologies using L-moment ratio diagrams, a robust statistical tool for characterizing traffic behavior. Results reveal that 5G offers a more stable and predictable bitrate distribution, whereas 4G exhibits higher variability, particularly in mobile scenarios, degrading QoS. Additionally, results also show inconsistencies in the dataset mainly due to the presence of traffic from non-declared networks, highlighting the need for more refined and validated datasets for future studies. Understanding these differences is also crucial for identifying current challenges and defining optimization strategies that will guide the development of next generation networks, ensuring more stable and efficient performance in dynamic, high-demand environments.