A Deep Learning-Based Approach to Power Minimization in Multi-Carrier NOMA with SWIPT
- Submitting institution
-
The University of Manchester
- Unit of assessment
- 12 - Engineering
- Output identifier
- 135145321
- Type
- D - Journal article
- DOI
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10.1109/access.2019.2895201
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 17450
- Volume
- 7
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- January
- Year of publication
- 2019
- 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
-
5
- Research group(s)
-
F - EEE
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- A derivation and the parameters in this work has been adopted by a joint team from Northwestern Polytechnical University, China, Queen Mary University of London, and University of Houston USA (10.1109/LWC.2020.3023108).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
