A Machine Learning Approach to Evolving an Optimal Propagation Model for Last Mile Connectivity using Low Altitude Platforms
- Submitting institution
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Brunel University London
- Unit of assessment
- 12 - Engineering
- Output identifier
- 313-203753-5148
- Type
- D - Journal article
- DOI
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10.1016/j.comcom.2019.04.001
- Title of journal
- Computer Communications
- Article number
- -
- First page
- 9
- Volume
- 142
- Issue
- -
- ISSN
- 0140-3664
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2019
- URL
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https://doi.org/10.1016/j.comcom.2019.04.001
- 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
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1
- Research group(s)
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4 - Sensors & Digital Systems
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The primary motivation behind this research is evolution of optimal air-to-ground propagation models for the last mile which is one of the major issues that affect the performance of such hybrid ad hoc networks and the outcomes are optimisation techniques that are supported by data analytics and machine learning. Outcomes led to Saudi Government funding for Almaki to continue work in this area as PDRF under Angelides supervision for a period of two years starting in September 2018.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -