Higher-Order Cyclostationarity Detection for Spectrum Sensing

Recent years have shown a growing interest in the concept of Cognitive Radios (CRs), able to access portions of the electromagnetic spectrum in an opportunistic operating way. Such systems require efficient detectors able to work in low Signal-to-Noise Ratio (SNR) environments, with little or no information about the signals they are trying to detect. Energy detectors are widely used to perform such blind detection tasks, but quickly reach the so-called SNR wall below which detection becomes impossible Tandra (2005). Cyclostationarity detectors are an interesting alternative to energy detectors, as they exploit hidden periodicities present in man-made signals, but absent in noise. Such detectors use quadratic transformations of the signals to extract the hidden sine-waves. While most of the literature focuses on the second-order transformations of the signals, we investigate the potential of higher-order transformations of the signals. Using the theory of Higher-Order Cyclostationarity (HOCS), we derive a fourth-order detector that performs similarly to the second-order ones to detect linearly modulated signals, at SNR around 0 dB, which may be used if the signals of interest do not exhibit second-order cyclostationarity. More generally this paper reviews the relevant aspects of the cyclostationary and HOCS theory, and shows their potential for spectrum sensing.

[1]  J. I. Mararm,et al.  Energy Detection of Unknown Deterministic Signals , 2022 .

[2]  Rahul Tandra,et al.  Fundamental limits on detection in low SNR , 2005 .

[3]  William A. Gardner,et al.  The cumulant theory of cyclostationary time-series. I. Foundation , 1994, IEEE Trans. Signal Process..

[4]  William A. Gardner,et al.  Signal interception: performance advantages of cyclic-feature detectors , 1992, IEEE Trans. Commun..

[5]  Jeffrey H. Reed,et al.  Cyclostationary Approaches to Signal Detection and Classification in Cognitive Radio , 2007, 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[6]  Linda Doyle,et al.  Cyclostationary Signatures in Practical Cognitive Radio Applications , 2008, IEEE Journal on Selected Areas in Communications.

[7]  R.W. Brodersen,et al.  Cyclostationary Feature Detector Experiments Using Reconfigurable BEE2 , 2007, 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[8]  Georgios B. Giannakis,et al.  Statistical tests for presence of cyclostationarity , 1994, IEEE Trans. Signal Process..

[9]  William A. Gardner,et al.  Statistical spectral analysis : a nonprobabilistic theory , 1986 .

[10]  Marko Kosunen,et al.  Implementation of Cyclostationary Feature Detector for Cognitive Radios , 2009, 2009 4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications.

[11]  Ian F. Akyildiz,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey , 2006, Comput. Networks.

[12]  William A. Gardner,et al.  Signal interception: a unifying theoretical framework for feature detection , 1988, IEEE Trans. Commun..

[13]  Y. Bar-Ness,et al.  Higher-order cyclic cumulants for high order modulation classification , 2003, IEEE Military Communications Conference, 2003. MILCOM 2003..

[14]  William A. Gardner,et al.  The cumulant theory of cyclostationary time-series. II. Development and applications , 1994, IEEE Trans. Signal Process..