A new cumulant estimator in multipath fading channels for digital modulation classification

Abstract

In this study, the authors consider the problem of digital modulation classification in a multipath channel using the cumulant-based method. A new estimator is proposed to estimate the fourth-order cumulant of transmitted symbol in a multipath channel with the help of the fourth-order cross-cumulants of the received symbols. The proposed estimator does not need the estimation of the multipath channel coefficients and is shown to be asymptotically unbiased. The channel order is an important factor to affect the estimator. The impact of the channel order mismatch on the estimator is analysed and it is proved that the estimator keeps unbiased in the case of the overestimation of the channel order. The simulated experiments are made to verify the cumulant-based classifier using the new estimator and the results show that it attains better performance for digital modulation classification than the two existing cumulant-based classifiers in a multipath channel.

Novel Modulation Recognition for WFRFT-Based System Using 4th-Order Cumulants

“…We can find that when the WFRFT order α comes closer to 0,C 42 gets larger, which confirms that it's reasonable to get the optimal α r by minimizing the 4th-order Cumulants of the receiving signal, C 42 in Eq. (26). When α > 0.5,C 42 gets smaller(approaching zero).…”

“…And HOC has also been widely implemented in all kinds of communications, i.e. Multiple-Input and Multiple-Output (MIMO) [25], multi-path fading [26], Cognitive Radio(CR) [27], and satellite communications [28]. Thus, it's tempting to recognize the WFRFT-based signals based on HOC.…”

Kuiper test based modulation level classification under unknown frequency selective channels

“…The classification techniques in [1], [5], [8], [9] do not consider the impact of the frequency selective channel. The works in [6], [7], [10]- [12] have developed classification techniques under frequency selective channels. However, these techniques have the following shortcomings.…”

“…However, these techniques have the following shortcomings. The classifiers in [6], [7], [10] utilize fourth-and sixth-order cumulants in order to classify between 4-, 16-, and 64-QAM. These cumulants cannot be used to classify between higher order M-PSK (M > 2) signals because the cumulant values for those modulations are the same [4].…”

Automatic Modulation Classification Using a Support Vector Machine-Based Pattern Recognition Algorithm

“…From the detailed literature, it is also found that some of the other approaches have also been developed for AMC. They are statistical approaches [17][18][19][20][21][22], where different statistical features of the signal, such as correlations, moments, and cumulants in the complex envelope of the signal, are extracted and then a multilevel classification algorithm is applied for classifying the signals. The accuracy of the Back Propagation Neural Network (BPNN) is higher than that of the Kolmogorov Smirnov (KS) and higher-order statistics (HoS) approaches [23].…”