Blind Signal Modulation Recognition through Density Spread of Constellation Signature
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Blind Signal Modulation Recognition through Density Spread of Constellation Signature Gaurav Jajoo1 · Yogesh Kumar1 · Ashok Kumar1 · Sandeep Kumar Yadav1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Automatic recognition of modulation scheme in blind environment plays a key role in many communication applications. A hierarchical and local density (HLD) approach is proposed to classify eight modulation schemes in a two stage process. In the first stage, the domain of modulation schemes (FSK, ASK, PSK, and QAM) is identified. FSK is identified based on feature extracted through complex envelope of downconverted signal. ASK scheme is identified using linear regression error. PSK and QAM modulation schemes are recognized based on the ratio of sixth and fourth order cumulant. In the latter stage, the order of modulation (ASK, PSK, and QAM) is classified through its respective ideal constellation points. HLD can correctly identify 2ASK, 4ASK, and QPSK modulation schemes in AWGN channel above 8 dB SNR and the other modulation schemes (8ASK, 8PSK, 16QAM, and 64QAM) above 16dB SNR. HLD is implemented in NI labVIEW and validated on the signals generated through PXIe-5673 and received using NI PXIe-5661. The proposed HLD classifier does not require any training to set thresholds as compared to more complex SVM, KNN, and Naive Bayes Classifier based techniques and shows an improved accuracy. Keywords Blind signal · Carrier frequency offset · Cumulants · Linear regression · Modulation classification
* Gaurav Jajoo [email protected] Yogesh Kumar [email protected] Ashok Kumar [email protected] Sandeep Kumar Yadav [email protected] 1
Department of Electrical Engineering, Indian Institute of Technology Jodhpur, Jodhpur, Rajasthan, India
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1 Introduction Automatic modulation classification (AMC) finds the modulation type of the unknown received signal corrupted with noise in the channel. It is a step between detection of a signal and its demodulation. AMC has many military applications like for preparing jamming signal, to disrupt the communication between hostile communication systems, etc. AMC also gets attention in civil applications like link adaptation at transmitter and automatic modulation classifier deployed at receiver side. AMC seems to be a good solution for the problem of spectrum shortage and can be used in the design of software-defined radio and cognitive radio technologies [11]. Solution to AMC problem is achieved in two stages, preprocessing of signal and modulation classification. Different parameters of unknown signal like spectrum information, carrier frequency, symbol rate, channel parameters, etc are estimated first and modulation classifier is deployed at second stage. In literature, approach for modulation classification is mainly grouped into two categories: likelihood-based (LB) and feature-based (FB). LB approach tackles the classification problem as testing of multiple hypotheses. Hypotheses for all considered modulation s
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