Implementing Deep Learning Techniques in 5G IoT Networks for 3D Indoor Positioning: DELTA (DeEp Learning-Based Co-operaTive Architecture)
- Submitting institution
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Edinburgh Napier University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2689003
- Type
- D - Journal article
- DOI
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10.3390/s20195495
- Title of journal
- Sensors
- Article number
- 5495
- First page
- -
- Volume
- 20
- Issue
- 19
- ISSN
- 1424-8220
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2020
- URL
-
-
- Supplementary information
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-
- 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
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6
- Research group(s)
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-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposes a novel cooperative learning method to improve localisation for 3D indoor positioning for 5G IoT that uses multiple deep neural networks.
Using a practical setup of a 5G wireless sensor network, we construct a 3D radiomap database and demonstrate an improvement over the state of the art in terms of distance error (below 1.5m)
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -