Hierarchical digital modulation classification using cumulants

By Ananthram Swami, Brian M. Sadler. IEEE Transactions on Communications, 2000

1,098 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b03fa2f4aeb84d9e7ef2c565d18e4c3acc2e1050

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Citations in the corpus

Novel Automatic Modulation Classification Using Cumulant Features for Communications via Multipath Channels (2008, methodology): “Except that the negative impacts of multipath fading channels on AMC are discussed in [6], additional equalizers are needed in [18] or perfect channel knowledge is required in [4], [19], to the best of our knowledge, none of the existing AMC techniques without equalization is capable of tackling thi”
Dynamic Spectrum Access: Signal Processing, Networking, and Regulatory Policy (2006, neutral): “In addition, going beyond detection, signal parameters or features may be estimated, and signal classiﬁers employed, such as modulation classiﬁcation [46].”
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].”
Likelihood-Ratio Approaches to Automatic Modulation Classification (2011, methodology): “Research has been conducted in classifying analog, digital, and orthogonal frequency-division multiplexing (OFDM) modulation schemes using maximumlikelihood (ML) test, waveform zero crossing measurement [29], spectral feature [51], wavelet analysis [23], [68], signal cyclostationarity [36], [50], as”
A Survey of Dynamic Spectrum Access (2007, neutral): “In addition, going beyond detection, signal parameters or features may be estimated and signal classifiers employed, such as modulation classification [41].”
Survey of automatic modulation classification techniques: classical approaches and new trends (2007, methodology): “AMC algorithms proposed in the literature employ information extracted from either the received baseband waveform [1]-[9], [14]-[28], [41]-[54], [58]-[65] or intermediate frequency [32]-[38], [55], [88].”
Automatic Modulation Classification: A Deep Learning Enabled Approach (2018, methodology): “Since good statistical features can provide robust performance with low complexity [5], feature extraction-based AMC methods have been studied in many existing papers [6], [7], such as cyclic feature [8], higher order moments and cumulants [9]–[11], wavelet transforms [12], [13], et al.”
Automatic Modulation Classification Using Combination of Genetic Programming and KNN (2012, methodology): “In [21] Swami and Sadler presented a detailed analysis about the classification of different digital modulation schemes using cumulants.”
Modulation Classification Based on Signal Constellation Diagrams and Deep Learning (2019, neutral): “1) Cumulant: A traditional signal processing algorithm using the fourth-order cumulant ̃ C40 as the classification statistics [15].”