Blind Signal Modulation Recognition through Density Spread of Constellation Signature

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.