Learning and Adaptation in Cognitive Radios Using Neural Networks
Abstract
The estimation of the communication performance achievable with respect to environmental factors and configuration parameters plays a key role in the optimization process performed by a Cognitive Radio according to the original definition by Mitola [1]. In this paper we propose the use of Multilayered Feedforward Neural Networks as an effective technique for real-time characterization of the communication performance which is based on measurements carried out by the device and therefore offers some interesting learning capabilities.
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ML-based AP selection for network resource allocation outperforms AP selection mechanisms based on SNR, load based scheme and beacon delay scheme, especially in dynamic environments.
“Undoubtedly, the ML-based AP selection for network resource allocation, outperforms AP selection mechanisms based on the signal to noise ratio (SNR), the load based scheme and the beacon delay scheme, especially in dynamic environments.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [35]
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本, 刊. (2012) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/csb2012-57-12-1073
董, 旭., 魏, 胜., & 李, 颖. (2012) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/csb2012-57-12-1067
Artificial Intelligence Techniques in Cognitive Radio Networks Applications
Pal, K. (2024) · Advances in wireless technologies and telecommunication book series · doi:10.4018/979-8-3693-2893-4.ch003
- Authors
- Baldo, N., & Zorzi, M.
- Affiliations
- University of Padua (IT)
- ORCID
- Zorzi, Michele
- Venue
- 2008 5th IEEE Consumer Communications and Networking Conference
- Publisher
- IEEE
- Published
- 2008
- Pages
- 998-1003
- ISSN
- 2331-9852 (ISSN-L)
- OpenAlex
- W2118292831
- Cited by
- 102
- References
- 23
- Citations by year
- 2024: 1 · 2023: 1 · 2022: 1 · 2021: 2 · 2020: 5 · 2019: 1 · 2018: 6 · 2017: 5 · 2016: 10 · 2015: 6 · 2014: 8 · 2013: 4 · 2012: 25
- FWCI
- 6.94
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Consumer Communications and Networking Conference (publishedVersion)
- Padua Research Archive (University of Padova) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Cognitive Radio Networks and Spectrum Sensing
- Physical Sciences › Computer Science › Artificial Intelligence › Wireless Signal Modulation Classification
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Communication Networks Research
- Keywords
- cognitive radio, real-time characterization, adaptive learning, network optimization
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