Modulation detection is important to many communication and electronic warfare applications. Recent developments in cognitive radio (CR) and dynamic spectrum access (DSA) network have also brought much attention to modulation detection of unknown radio frequency (RF) signals. It is well known that using second-order cyclostationary features, BPSK modulation can be easily distinguished from higher order modulations such as QPSK and QAM. However, these higher order modulations exhibit similar second-order cyclostationary features, thus theses features cannot be employed to further distinguish higher order modulations. To accurately detect and classify higher order modulations, higher order features such as higher order cumulants are desired. In this paper, we build a automatic blind hierarchical modulation detector to successfully classify the modulations of RF signals. Moreover, we use software defined radio (SDR) to implement and demonstrate a practical blind hierarchical modulation detector which can accurately distinguish among three popular modulations, i.e., BPSK, QPSK and 16-QAM. Specifically, second-order cyclostationary features using detailed spectral coherent function (SOF) are applied first to distinguish between BPSK modulation and non-BPSK modulations (e.g., QPSK and 16-QAM modulations) at first level of the hierarchical modulation detector. Next, the fourth-order cumulant feature is employed to the non-BPSK RF signals to distinguish between QPSK and 16-QAM. The SDR based modulation detector does not require any priori information of the RF transmission, and executes accurate detection in real time. Demonstrations in AWGN channel and realistic multi-path fading channel confirm the effectiveness and efficiency of the proposed modulation detector.
[1]
D. Brillinger.
Time series - data analysis and theory
,
1981,
Classics in applied mathematics.
[2]
M. Melamed.
Detection
,
2021,
SETI: Astronomy as a Contact Sport.
[3]
Michael A. Temple,et al.
Novel overlay/underlay cognitive radio waveforms using SD-SMSE framework to enhance spectrum efficiency-part II: analysis in fading channels
,
2010,
IEEE Transactions on Communications.
[4]
Brian M. Sadler,et al.
Hierarchical digital modulation classification using cumulants
,
2000,
IEEE Trans. Commun..
[5]
William Gardner,et al.
Spectral Correlation of Modulated Signals: Part I - Analog Modulation
,
1987,
IEEE Transactions on Communications.
[6]
Athanasios V. Vasilakos,et al.
Novel overlay/underlay cognitive radio waveforms using SD-SMSE framework to enhance spectrum efficiency- part i: theoretical framework and analysis in AWGN channel
,
2009,
IEEE Transactions on Communications.
[7]
William A. Gardner,et al.
Spectral Correlation of Modulated Signals: Part II - Digital Modulation
,
1987,
IEEE Transactions on Communications.
[8]
Hüseyin Arslan,et al.
A survey of spectrum sensing algorithms for cognitive radio applications
,
2009,
IEEE Communications Surveys & Tutorials.