Heuristically Accelerated Reinforcement Learning for Dynamic Secondary Spectrum Sharing
- Submitting institution
-
University of York
- Unit of assessment
- 12 - Engineering
- Output identifier
- 55024183
- Type
- D - Journal article
- DOI
-
10.1109/ACCESS.2015.2507158
- Title of journal
- IEEE Access
- Article number
- 7350209
- First page
- 2771
- Volume
- 3
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Out of scope for open access requirements
- Month of publication
- December
- Year of publication
- 2015
- 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
-
2
- Research group(s)
-
A - Communication Technologies
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work on spectrum sharing with a Helikite inspired the York Helikite Testbed, which won the National Instruments EMEA Engineering Impact Award 2018 Wireless Communications. The first demonstration of accelerated reinforcement learning enabling spectrum sharing. It inspired collaboration with a Huawei, Moscow researcher (Contact: Huawei project lead). The Orange France contract exploited it with a high altitude platform system (Mesh-HAP, Contact: Orange project lead) and the QinetiQ DSpX contract modified it for military ad hoc networks (CSIIS 2-1-48, Contact: Qinetiq project lead). It informed the development of QL-MAC by researchers at Eindhoven University.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
