Analysis of different functional split options for XR traffic in 5G-LENA

Virtualized Radio Access Networks (vRAN) allow the disaggregation of the RAN protocol stack into different network units. This disaggregation depends on the selected Functional Split (FS), ranging from Option 1 to Option 8. The possible combination of this virtualization paradigm with different FS configurations could enhance the agility, flexibility, and scalability of 5G and beyond networks. Several studies exploring FSs use open-source platforms such as srsRAN, which provide full-stack 4G and 5G RAN software. However, these platforms have limitations for large-scale network analysis and usually require hardware for realistic testing. In contrast, system-level simulators such as ns-3 5G-LENA accurately model key 5G RAN functionalities and provide a flexible, scalable, and entirely software-based environment to implement, test, and evaluate a wide range of features. Based on the above, in this work we present a software-based model for the ns-3 5G-LENA simulator that allows simulating various FS options. We study and evaluate these FS options, specifically Options 6, 7.3, 7.2, and 7.1. Moreover, we analyze their impact on the end-to-end system performance using eXtended Reality (XR) traffic and under diverse network conditions. Our analysis shows that an appropriate FS selection can improve overall performance, helping the system more effectively handle XR traffic demands across different network conditions. Specifically, when Fronthaul (FH) and bandwidth are not limiting, all FS configurations yield similar throughput and latency, with XR delays mostly below 11 ms and data rates closely matching the offered load. Under FH constraints, lower FSs (e.g., Options 6 or 7.3) are preferable due to large, bursty XR packets. Otherwise, for standardized Open RAN (O-RAN) 7.2 or 7.1 FSs, delays may exceed 50 ms and data rates degrade, significantly affecting XR traffic performance. This work provides the research community with an accessible tool for studying FSs in realistic 5G environments.