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Low-complexity cyclostationary-based modulation classifying algorithm
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DOI：10.1016/j.aeue.2017.02.008.png)
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摘要
En 中文
In this paper a low-complexity cyclostationary-based modulation classifier is presented, which is capable of distinguishing between OFDM, GFSK and QPSK modulations. The classifier computes and analyses the cyclic autocorrelation of the received signals in an implementation-efficient manner. Instead of computing a high number of values of the cyclic autocorrelation like other implementations, which leads to a non-implementable solution, it computes 3 values, allowing a real-time hardware implementation of the algorithms at a limited cost. The performance is evaluated through simulations in MATLAB, under white Gaussian noise and receiver impairments such as frequency offset, I/Q imbalance and DC offset. In order to assess the actual performance and complexity of the classifying algorithm, an FPGA-based implementation is presented in this document. Performance results with real signals are provided, which validate the ones obtained through simulations. (C) 2017 Elsevier GmbH. All rights reserved.
Keyword:
Cognitive radio
Cyclostationary detector
FPGA implementation
Modulation classifier
Spectrum sensing
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3.2
论文数:
5.8K
被引数:
9.6K
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Modulation Classification of Single-Input Multiple-Output Signals Using Asynchronous Sensors使用异步传感器对单输入多输出信号进行调制分类.png)
IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
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