Novel Automatic Modulation Classification Using Cumulant Features for Communications via Multipath Channels

By Hsiao-Chun Wu, Mohammad Saquib, Zhifeng Yun. IEEE Transactions on Wireless Communications, 2008

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How researchers see this paper

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Automatic Modulation Classification Using Combination of Genetic Programming and KNN (2012, result): “Wu, Saquib and Yun [26] presented results for QAM4, QAM16 and QAM64 using higher order statistics.”
On the likelihood-based approach to modulation classification (2009, methodology): “The QHLRT which employs method of moments (MoM) estimates of the unknown parameters (QHLRT-MoM) is investigated, and its classification performance compared to QHLRT-UB, ALRT-UB, quasi Log-Likelihood Ratio (qLLR) [4], [5] and fourth-order cumulant based algorithms [12], [13].”
Automatic Modulation Classification: A Deep Learning Enabled Approach (2018, unknown): “obviously deteriorated when |Δθc | ∈ [6◦, 9◦], and the ML-AMC gets even worse when SNR increases at the region of [8, 10] dB (|Δθc | = 9◦).”