Cognitive networks: adaptation and learning to achieve end-to-end performance objectives

By Ryan W. Thomas, Daniel H. Friend, Luiz A. DaSilva, Allen B. Mackenzie. IEEE Communications Magazine, 2006

519 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Intelligent Transportation Systems (2010, unknown): “In response to the above, traffic assessment and management has been established as a key service that should be offered in the area of transportation by Information and Communication Technologies [3]–[ 5 ].”
Cognitive radio networking and communications: an overview (2011, background): “The control and autonomous adaptation of PHY-layer parameters have also been considered by several research groups, typically centering at the concept of cognitive engine [194], [195] or a cognitive resource manager framework [196].”
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, background): “to-end goals, where the overall network conditions are monitored, and then decisions are made based on the perceived conditions as well as on the feedback and experience gleaned from previous actions [105]. The network’s cognitive capability relies on a range of advanced techniques, such as knowledg”
Task-based End-to-end Model Learning (2017, unknown): “Generally speaking, this term refers to systems where the end goal of the machine learning process is directly predicted from the raw inputs (e.g. LeCun et al., 2005; Thomas et al., 2006).”
Task-based End-to-end Model Learning in Stochastic Optimization (2017, background): “Generally speaking, this term refers to systems where the end goal of the machine learning process is directly predicted from raw inputs [e.g. 7, 8].”