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 April 30, 2024)
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O-RAN with Machine Learning in ns-3
Published
Author(s)
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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
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Keywords
O-RAN, ns-3, Machine Learning, Modeling and Simulation, LTE, ONN
Citation
Created June 28, 2023, Updated August 10, 2023