Please use this identifier to cite or link to this item:
https://doi.org/10.1155/2014/209810
DC Field | Value | |
---|---|---|
dc.title | Application of reinforcement learning in cognitive radio networks: Models and algorithms | |
dc.contributor.author | Yau, K.-L.A | |
dc.contributor.author | Poh, G.-S | |
dc.contributor.author | Chien, S.F | |
dc.contributor.author | Al-Rawi, H.A.A | |
dc.date.accessioned | 2020-11-19T07:18:17Z | |
dc.date.available | 2020-11-19T07:18:17Z | |
dc.date.issued | 2014 | |
dc.identifier.citation | Yau, K.-L.A, Poh, G.-S, Chien, S.F, Al-Rawi, H.A.A (2014). Application of reinforcement learning in cognitive radio networks: Models and algorithms. Scientific World Journal 2014 : 209810. ScholarBank@NUS Repository. https://doi.org/10.1155/2014/209810 | |
dc.identifier.issn | 23566140 | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/183716 | |
dc.description.abstract | Cognitive radio (CR) enables unlicensed users to exploit the underutilized spectrum in licensed spectrum whilst minimizing interference to licensed users. Reinforcement learning (RL), which is an artificial intelligence approach, has been applied to enable each unlicensed user to observe and carry out optimal actions for performance enhancement in a wide range of schemes in CR, such as dynamic channel selection and channel sensing. This paper presents new discussions of RL in the context of CR networks. It provides an extensive review on how most schemes have been approached using the traditional and enhanced RL algorithms through state, action, and reward representations. Examples of the enhancements on RL, which do not appear in the traditional RL approach, are rules and cooperative learning. This paper also reviews performance enhancements brought about by the RL algorithms and open issues. This paper aims to establish a foundation in order to spark new research interests in this area. Our discussion has been presented in a tutorial manner so that it is comprehensive to readers outside the specialty of RL and CR. © 2014 Kok-Lim Alvin Yau et al. | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.source | Unpaywall 20201031 | |
dc.subject | article | |
dc.subject | artificial intelligence | |
dc.subject | cognition | |
dc.subject | cognitive radio network | |
dc.subject | decision making | |
dc.subject | environmental factor | |
dc.subject | learning | |
dc.subject | learning algorithm | |
dc.subject | reinforcement | |
dc.subject | reward | |
dc.subject | signal noise ratio | |
dc.subject | spectrum | |
dc.subject | wireless communication | |
dc.subject | algorithm | |
dc.subject | artificial intelligence | |
dc.subject | computer network | |
dc.subject | human | |
dc.subject | theoretical model | |
dc.subject | Algorithms | |
dc.subject | Artificial Intelligence | |
dc.subject | Computer Communication Networks | |
dc.subject | Humans | |
dc.subject | Models, Theoretical | |
dc.subject | Reinforcement (Psychology) | |
dc.type | Article | |
dc.contributor.department | DEPARTMENT OF COMPUTER SCIENCE | |
dc.description.doi | 10.1155/2014/209810 | |
dc.description.sourcetitle | Scientific World Journal | |
dc.description.volume | 2014 | |
dc.description.page | 209810 | |
Appears in Collections: | Elements Staff Publications |
Show simple item record
Files in This Item:
File | Description | Size | Format | Access Settings | Version | |
---|---|---|---|---|---|---|
10_1155_2014_209810.pdf | 1.83 MB | Adobe PDF | OPEN | None | View/Download |
This item is licensed under a Creative Commons License