ChARM: NextG Spectrum Sharing Through Data-Driven Real-Time O-RAN Dynamic Control Article Swipe
L. Baldesi
,
Francesco Restuccia
,
Tommaso Melodia
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2201.06326
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2201.06326
Today's radio access networks (RANs) are monolithic entities which often operate statically on a given set of parameters for the entirety of their operations. To implement realistic and effective spectrum sharing policies, RANs will need to seamlessly and intelligently change their operational parameters. In stark contrast with existing paradigms, the new O-RAN architectures for 5G-and-beyond networks (NextG) separate the logic that controls the RAN from its hardware substrate, allowing unprecedented real-time fine-grained control of RAN components. In this context, we propose the Channel-Aware Reactive Mechanism (ChARM), a data-driven O-RAN-compliant framework that allows (i) sensing the spectrum to infer the presence of interference and (ii) reacting in real time by switching the distributed unit (DU) and radio unit (RU) operational parameters according to a specified spectrum access policy. ChARM is based on neural networks operating directly on unprocessed I/Q waveforms to determine the current spectrum context. ChARM does not require any modification to the existing 3GPP standards. It is designed to operate within the O-RAN specifications, and can be used in conjunction with other spectrum sharing mechanisms (e.g., LTE-U, LTE-LAA or MulteFire). We demonstrate the performance of ChARM in the context of spectrum sharing among LTE and Wi-Fi in unlicensed bands, where a controller operating over a RAN Intelligent Controller (RIC) senses the spectrum and switches cell frequency to avoid Wi-Fi. We develop a prototype of ChARM using srsRAN, and leverage the Colosseum channel emulator to collect a large-scale waveform dataset to train our neural networks with. Experimental results show that ChARM achieves accuracy of up to 96% on Colosseum and 85% on an over-the-air testbed, demonstrating the capacity of ChARM to exploit the considered spectrum channels.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2201.06326
- https://arxiv.org/pdf/2201.06326
- OA Status
- green
- Cited By
- 4
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4221143193
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4221143193Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2201.06326Digital Object Identifier
- Title
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ChARM: NextG Spectrum Sharing Through Data-Driven Real-Time O-RAN Dynamic ControlWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-01-17Full publication date if available
- Authors
-
L. Baldesi, Francesco Restuccia, Tommaso MelodiaList of authors in order
- Landing page
-
https://arxiv.org/abs/2201.06326Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2201.06326Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2201.06326Direct OA link when available
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Computer science, Context (archaeology), Control channel, Ran, Computer network, Base station, Paleontology, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2023: 1, 2022: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.operational | 41, 118 |
| abstract_inverted_index.operations. | 23 |
| abstract_inverted_index.parameters. | 42 |
| abstract_inverted_index.performance | 184 |
| abstract_inverted_index.unprocessed | 136 |
| abstract_inverted_index.Experimental | 246 |
| abstract_inverted_index.fine-grained | 71 |
| abstract_inverted_index.interference | 101 |
| abstract_inverted_index.modification | 150 |
| abstract_inverted_index.over-the-air | 263 |
| abstract_inverted_index.5G-and-beyond | 54 |
| abstract_inverted_index.Channel-Aware | 82 |
| abstract_inverted_index.architectures | 52 |
| abstract_inverted_index.demonstrating | 265 |
| abstract_inverted_index.intelligently | 38 |
| abstract_inverted_index.unprecedented | 69 |
| abstract_inverted_index.O-RAN-compliant | 88 |
| abstract_inverted_index.specifications, | 164 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |