Applications of Machine Learning to Cognitive Radio Networks

By T. Clancy, Joe Hecker, Erich P. Stuntebeck, Tim O'Shea. IEEE wireless communications, 2007

339 citations recorded by the source corpus.

Cognitive radio offers the promise of intelligent radios that can learn from and adapt to their environment. To date, most cognitive radio research has focused on policy-based radios that are hard-coded with a list of rules on how the radio should behave in certain scenarios. Some work has been done on radios with learning engines tailored for very specific applications. This article describes a concrete model for a generic cognitive radio to utilize a learning engine. The goal is to incorporate the results of the learning engine into a predicate calculus-based reasoning engine so that radios can remember lessons learned in the past and act quickly in the future. We also investigate the differences between reasoning and learning, and the fundamentals of when a particular application requires learning, and when simple reasoning is sufficient. The basic architecture is consistent with cognitive engines seen in AI research. The focus of this article is not to propose new machine learning algorithms, but rather to formalize their application to cognitive radio and develop a framework from within which they can be useful. We describe how our generic cognitive engine can tackle problems such as capacity maximization and dynamic spectrum access.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/3493e0285fe329a710f54be2ef82350fdaafc991

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Citations in the corpus

Security in Cognitive Radio Networks: Threats and Mitigation (2008, background): “It should also be noted that such radios theoretically do not require a learning engine, and their behavior can be reduced to the case-based reasoning of a policy radio [9].”
Convolutional Radio Modulation Recognition Networks (2016, background): “This considers a number of real world eﬀects which are non-trivial to model: modulation by a residual carrier random walk process, n Lo ( t ), resampling by a residual clock oscillator random walk, n Clk ( t ), convolution with a time varying rotating non-constant amplitude channel impulse response…”
Contour Stella Image and Deep Learning for Signal Recognition in the Physical Layer (2020, background): “3024610 as these are usually collected from various sources with different data formats and complex correlation structures [3], [4].”
A Survey on Security Threats and Detection Techniques in Cognitive Radio Networks (2013, background): “A PUEA can be more effective on learning CRs [76], as these radios build a long-term behavior based on their observations from the environment.”
A Survey on Machine-Learning Techniques in Cognitive Radios (2013, methodology): “of the inputs on the outputs of a given system are not known [69].”
Team Deep Neural Networks for Interference Channels (2018, methodology): “In [15] a framework to incorporate machine learning in cognitive radio is described while its application to coding is discussed in [16], [17].”
Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handovers (2017, background): “3 for performing intelligent handovers via VLR and HLR information [15].”
Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019, methodology): “Therefore, ML methods have thoroughly been reviewed for cognitive radios, see e.g., [235].”
State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems (2017, background): “The work in [243] points out the signiﬁ-cant difference between reasoning and learning in CRNs.”