A Deep Learning Framework for Optimization of MISO Downlink Beamforming
- Submitting institution
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London South Bank University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 279970
- Type
- D - Journal article
- DOI
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10.1109/TCOMM.2019.2960361
- Title of journal
- IEEE Transactions on Communications
- Article number
- -
- First page
- 1866
- Volume
- 68
- Issue
- 3
- ISSN
- 0090-6778
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2019
- URL
-
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8935405
- 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)
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B - Cognitive Systems Research Centre
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- In this paper proposed a deep learning framework for the optimization of downlink beamforming. This work was published in IEEE Transactions on communications, the impact factor of this paper is 5.646 and cited over 50 times. This work collaborates with University of Kent, The State University of New Jersey, Nanjing University of Posts and Telecommunications and Loughborough University. This work was supported in part by the UK Engineering and Physical Sciences Research Council (EPSRC, Grant No. EP/N007840/1), and the Leverhulme Trust Research Project Grant (Grant No. RPG-2017-129).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -