O-RAN with Machine Learning in ns-3

Published

June 28, 2023

Author(s)

Wesley Garey, Richard A. Rouil, Evan Black, Tanguy Ropitault, Weichao Gao

Abstract

The Open Radio Access Network (O-RAN) Alliance is the industry led standardization effort, with the sole purpose of evolving the Radio Access Network (RAN) to be more open, intelligent, interoperable, and autonomous to support the ever growing need of improved performance and flexibility in mobile networks. This paper introduces an extension to the network simulator, Network Simulator 3 (ns-3), that mimics the behavior and components of the O-RAN architecture that have been defined by the O-RAN Alliance. In this paper we will describe the O-RAN architecture, our model in ns-3, and a Long Term Evolution (LTE) use case that utilizes Machine Learning (ML) and its integration with ns-3. At the end of this paper, the reader will have a general understanding of O-RAN, and the capabilities of our fully-simulated contribution, so that it can be leveraged to evaluate and study O-RAN-based solutions.

Proceedings Title

Workshop on ns-3 (WNS3) 2023

Conference Dates

June 28-29, 2023

Conference Location

Ballston, VA, US

Pub Type

Conferences

Download Paper

https://doi.org/10.1145/3592149.3592157

Local Download

Keywords

O-RAN, ns-3, Machine Learning, Modeling and Simulation, LTE, ONN

Citation

Garey, W. , Rouil, R. , Black, E. , Ropitault, T. and Gao, W. (2023), O-RAN with Machine Learning in ns-3, Workshop on ns-3 (WNS3) 2023, Ballston, VA, US, [online], https://doi.org/10.1145/3592149.3592157, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=936291 (Accessed October 26, 2025)