Twinning Commercial Network Traces on Experimental Open RAN Platforms
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.
Authors
- Leonardo Bonati (ORCID: https://orcid.org/0000-0002-1511-1833)
- Michele Polese (ORCID: https://orcid.org/0000-0002-9740-134X)
- Salvatore D’Oro (ORCID: https://orcid.org/0000-0002-7690-0449)
- Tommaso Melodia (ORCID: https://orcid.org/0000-0002-2719-1789)
- Claudio Fiandrino (ORCID: https://orcid.org/0000-0002-4323-4355)
- Stefano Maxenti (ORCID: https://orcid.org/0009-0007-3628-1931)
- Ravis Shirkhani
Institutions
- Northeastern University (US)
- IMDEA Networks (ES)
Publication Details
- Published
- 2024-12-04
- DOI
- https://doi.org/10.1145/3636534.3697320
- Citations
- 1
- Primary Topic
- Advanced MIMO Systems Optimization
- Type
- article
- Field-Weighted Citation Impact
- 0.21
Funders
- National Science Foundation
- Ministerio de Ciencia, Innovación y Universidades
- European Commission
- National Telecommunications and Information Administration
- European Regional Development Fund
- Agencia Estatal de Investigación
- European Social Fund