Automatic Modulation Recognition of Communication Signals

Automatic modulation classification of digital modulations in presence of HF noise

“…For FSK modulation, rectangular pulse shape is commonly used [1]. The constellation points of digital modulation of order M considered in this article are given in Table 1 (in Table 1, f s is the sampling frequency and N f is the number of samples within one MFSK symbol duration); see also [9].…”

“…The noise term is often constructed by passing the BK/G noise through a band-limiting filter [9]. The bandwidth of this filter is set to 3 kHz for HF channel [37].…”

“…The SNR at the output of band-limiting filter is adjusted to the desired value by multiplying the noise term by a scaling factor, β. That is [9] …”

“…This block is followed by an AMC which contains a feature extractor and a classifier.An overwhelming number of proposed features exist in AMC literature. Some of the most popular features include instantaneous amplitude, phase, and frequency [7][8][9], statistical features such as higher order moments and cumulants [10][11][12][13], wavelets [14][15][16][17][18], spectral peaks [19], etc. The classifier block makes use of extracted features to identify signal modulation by applying a fixed threshold, or alternatively using a pattern recognition technique, such as artificial neural networks [20][21][22] or support vector machines [18].…”

“…To achieve the above objective, the following features are considered: the maximum value of power spectral density (PSD) of normalized-centered instantaneous amplitude, the maximum value of magnitude of discrete fourier transform (DFT) of kth power of received signal (Γ k ), and number of points in pre-defined ranges of partitioned signal constellation magnitude. The first feature is well known in the literature [7][8][9]. The second feature with k = 2, 4 has previously been proposed in [30] for blind estimation of frequency offset of PSK and QAM signals and in [31] for the classification of MPSK modulations.…”