A new approach to signal classification using spectral correlation and neural networks

By Albrecht J. Fehske, Joseph D. Gaeddert, Jeffrey H. Reed. First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005., 2005

497 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/90a2d4c20ef17adb79e108245f018969cb74b522

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Citations in the corpus

Cyclostationary Signatures in Practical Cognitive Radio Applications (2008, methodology): “Additionally, cyclostationary analysis has been extensively examined as a technique for achieving a wide range of tasks including signal detection [14], classification [19], synchronization [20], [21] and equalization [22].”
An Introduction to Deep Learning for the Physical Layer (2017, methodology): “Some recent methods take a step beyond this using pattern recognition on expert feature maps, such as the spectral coherence function or α -proﬁle, combined with NN-based classiﬁcation [54].”
Over-the-Air Deep Learning Based Radio Signal Classification (2017, background): “techniques, higher order statistics and cyclo-stationary moments [14]–[18] are among the most widely used features to compactly sense and detect signals with strong periodic components such”
Cognitive radio sensor networks (2009, methodology): “Feature detection [27] Requires a priori knowledge about PU transmissions.”
End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications (2017, background): “…are dominantly based on signal processing tools for communication [5] such as cyclostationary feature detection [6], sometimes in combination with traditional machine learning techniques [7] (e.g. support vector machines (SVM), decision trees, k-nearest neighbors (k-NN), neural networks, etc.).”
Advances in cognitive radio networks: A survey (2011, methodology): “By combining neural network for signal classification with cyclic spectral analysis, a more efficient and reliable classifier is developed in [39].”
A survey of spectrum sensing algorithms for cognitive radio applications (2009, methodology): “Neural network are utilized for classification in [75] while statistical tests are used in [72].”
CRAHNs: Cognitive radio ad hoc networks (2009, unknown): “In [25], the enhanced feature detection scheme combining cyclic spectral analysis with pattern recognition based on neural networks is proposed.”
NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey (2006, methodology): “Cyclostationary feature detection An alternative detection method is the cyclostationary feature detection [12] [22] [57].”
Cooperative spectrum sensing in cognitive radio networks: A survey (2011, methodology): “Although it requires a priori knowledge of the signal characteristics, cyclostationary feature detection is capable of distinguishing the CR transmissions from various types of PU signals [5,36].”
Cyclostationary Approaches to Signal Detection and Classification in Cognitive Radio (2007, background): “This results in generating the cycle frequency domain profile (CDP) (also called α-domain profile [7]).”