Twinning Commercial Network Traces on Experimental Open RAN Platforms

TL;DR

This paper presents a methodology to replicate real-world cellular traffic in Open RAN testbeds, providing a large dataset to facilitate AI-driven optimization and evaluation of RAN performance.

Contribution

The authors develop a novel approach to twin real cellular traffic in Open RAN environments and release a comprehensive dataset for research and testing.

Findings

Dataset includes over 500 hours of traffic data

Enables development of latency-sensitive Open RAN use cases

Demonstrates the feasibility of traffic twinning in experimental platforms

Abstract

While the availability of large datasets has been instrumental to advance fields like computer vision and natural language processing, this has not been the case in mobile networking. Indeed, mobile traffic data is often unavailable due to privacy or regulatory concerns. This problem becomes especially relevant in Open Radio Access Network (RAN), where artificial intelligence can potentially drive optimization and control of the RAN, but still lags behind due to the lack of training datasets. While substantial work has focused on developing testbeds that can accurately reflect production environments, the same level of effort has not been put into twinning the traffic that traverse such networks. To fill this gap, in this paper, we design a methodology to twin real-world cellular traffic traces in experimental Open RAN testbeds. We demonstrate our approach on the Colosseum Open RAN digital twin, and publicly release a large dataset (more than 500 hours and 450 GB) with PHY-, MAC-, and App-layer Key Performance Measurements (KPMs), and protocol stack logs. Our analysis shows that our dataset can be used to develop and evaluate a number of Open RAN use cases, including those with strict latency requirements.