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Cognitive networks: adaptation and learning to achieve end-to-end performance objectives
Abstract
In this article we advance the idea of a cognitive network, capable of perceiving current network conditions and then planning, learning, and acting according to end-to-end goals. Cognitive networks are motivated by the complexity, heterogeneity, and reliability requirements of tomorrow's networks, which are increasingly expected to self-organize to meet user and application objectives. We compare and contrast cognitive networks with related research on cognitive radios and cross-layer design. By defining cognitive networks, examining their relationship to other technologies, discussing critical design issues, and providing a framework for implementation, we aim to establish a foundation for further research and discussion
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Cited by
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Wang, J., Jiang, C., Zhang, H., Ren, Y., Chen, K.-C., & Hanzo, L. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2965856 · Cited by 486
Cognitive Network Inference through Bayesian Network Analysis
Quer, G., Meenakshisundaram, H., Tamma, B., Manoj, B. S., Rao, R., & Zorzi, M. (2010) · 2010 IEEE Global Telecommunications Conference GLOBECOM 2010 · doi:10.1109/glocom.2010.5683282 · Cited by 29
Metadata
- Authors
- Thomas, R., Friend, D., Dasilva, L., & Mackenzie, A.
- Venue
- IEEE Communications Magazine
- Published
- 2006-12
- Volume
- 44
- Issue
- 12
- Pages
- 51-57
- ISSN
- 0163-6804 (ISSN-L)
- OpenAlex
- W2159620355
- Cited by
- 467
- References
- 0
- FWCI
- 40.59
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Cognitive networks: adaptation and learning to achieve end-to-end performance objectives. https://app.cochat.ai/papers/10.1109/mcom.2006.273099
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- Thomas, R., Friend, D., Dasilva, L., & Mackenzie, A. (2006). Cognitive networks: adaptation and learning to achieve end-to-end performance objectives. IEEE Communications Magazine. https://doi.org/10.1109/mcom.2006.273099
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
