Survey of automatic modulation classification techniques: classical approaches and new trends

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Automatic Modulation Classification Using Combination of Genetic Programming and KNN (2012, methodology): “There are a lot of other techniques used for AMC and a detailed survey is presented in [11].”
Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors (2017, background): “In decision-theoretic approaches the modulation classification problem is presented as a multiple hypothesis testing problem [4].”
Likelihood-Ratio Approaches to Automatic Modulation Classification (2011, background): “gave the theoretical values of the lth-order/q-conjugate moments with l = 2, 4, 6, and 8 in [14].”
Automatic Modulation Classification: A Deep Learning Enabled Approach (2018, background): “As a result, automatic modulation classification (AMC) [1], [2] is of great importance for both civilian and military applications in achieving automatic receiver configuration, interference mitigation, and spectrum management.”
Modulation Classification Based on Signal Constellation Diagrams and Deep Learning (2019, neutral): “Previously, this issue has been handled through traditional signal processing [15], [16], the artificial neural network (ANN)”
An Overview of Signal Processing Techniques for Terahertz Communications (2020, background): “Despite the fact that modulation classification [202], [203] is a classical signal processing problem, its applicability to the THz-band [204] can prove to be particularly useful.”