Statistical spectral analysis: a nonprobabilistic theory
Statistical spectral analysis: a nonprobabilistic theory
Fundamentals of speech recognition
Fundamentals of speech recognition
Introduction to statistical signal processing with applications
Introduction to statistical signal processing with applications
Automatic identification of digital modulation types
Signal Processing
Distributed Detection and Data Fusion
Distributed Detection and Data Fusion
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
Combined likelihood power estimation and multiple hypothesis modulation classification
ASILOMAR '95 Proceedings of the 29th Asilomar Conference on Signals, Systems and Computers (2-Volume Set)
Chicago spectrum occupancy measurements & analysis and a long-term studies proposal
TAPAS '06 Proceedings of the first international workshop on Technology and policy for accessing spectrum
Experimental study of spectrum sensing based on energy detection and network cooperation
TAPAS '06 Proceedings of the first international workshop on Technology and policy for accessing spectrum
Sequential modulation classification of dependent samples
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 05
Principles of communication systems simulation with wireless applications
Principles of communication systems simulation with wireless applications
Cyclostationarity-based blind classification of analog and digital modulations
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
Higher-order cyclic cumulants for high order modulation classification
MILCOM'03 Proceedings of the 2003 IEEE conference on Military communications - Volume I
Search-efficient methods of detection of cyclostationary signals
IEEE Transactions on Signal Processing
Utilization of Location Information in Cognitive Wireless Networks
IEEE Wireless Communications
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
Spectrum sensing: A distributed approach for cognitive terminals
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications
IEEE Transactions on Wireless Communications
Grey systems theory applications to wireless communications
Analog Integrated Circuits and Signal Processing
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Cognitive radios have become a key research area in communications over the past few years. Automatic modulation classification (AMC) is an important component that improves the overall performance of the cognitive radio. Most modulated signals exhibit the property of cyclostationarity that can be exploited for the purpose of classification. In this paper, AMCs that are based on exploiting the cyclostationarity property of the modulated signals are discussed. Inherent advantages of using cyclostationarity based AMC are also addressed. When the cognitive radio is in a network, distributed sensing methods have the potential to increase the spectal sensing reliability, and decrease the probability of interference to existing radio systems. The use of cyclostationarity based methods for distributed signal detection and classification are presented. Examples are given to illustrate the concepts. The Matlab codes for some of the algorithms described in the paper are available for free download at http://filebox.vt.edu/user/bramkum.