Safeguard Network Slicing in 5G: A Learning Augmented Optimization Approach
- Submitting institution
-
University of Exeter
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 6343
- Type
- D - Journal article
- DOI
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10.1109/JSAC.2020.2999696
- Title of journal
- IEEE Journal on Selected Areas in Communications
- Article number
- -
- First page
- 1600
- Volume
- 38
- Issue
- 7
- ISSN
- 0733-8716
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Network slicing has been widely agreed as one of the enabling technologies to accommodate diverse services in industry (defined in 3GPP Release 16). This paper proposes an intelligent framework to safeguard the crucial network slicing applications in 5G, which is potentially applicable for many 5G services. This research is an important output from an EPSRC project (EP/R030863/1) and has enabled several outreach and public engagement activities including a Year-12 project with Exeter Mathematics School (received the interests and attention of both female and male students). This research has also led to an invited talk in an international workshop in 2020.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -