Abstract
The demand for bandwidth-critical applications has stimulated the research community not only to develop new ways of communication, but also to use the existing spectrum efficiently. Networks have become dynamic and heterogeneous. Receivers have received various signals that can be modulated differently. Automatic modulation classification (AMC) is a key procedure for present and next-generation communication networks, and facilitates the demodulation process at the receiver side. Under the presence of noise from the channel, the transmitter and receiver with its unknown parameters, such as carrier frequency, phase offset, signal power, and timing information, have become cumbersome because detecting the modulation scheme of the received signal is a complicated procedure. Two main methods, namely maximum likelihood functions and the signal statistical feature-based (FB) approach, are used for the automatic classification of modulated signals. In this study, a comprehensive survey of various modulation techniques based on FB approach is conducted. In this research, a number of basic features that are usually used in determining and discriminating modulation types were investigated. The classifier that was used in the discrimination process is studied in detail and compared to other types of classifiers to help the reader determine the limitations associated with the FB approach. Both classifiers and basic features were compared, and their advantages and disadvantages were investigated based on previous researches to determine the best type of classifier and the set of features in relation to each discrimination environment. This work serves as a guide for researchers of AMC to determine the suitable features and algorithms.
Highlights
With the recent advancements in telecommunication technologies, various bandwidth-critical applications have attracted the attention of users
Lower- and higher-order modulation schemes can be used for each category, such as binary frequency shift keying (BFSK), 16-quadrature amplitude modulation (QAM), and quadrature phase shift keying (QPSK) [18,19,20,21,22,23,24,25,26]
A fourth-order cyclic was used to identify amplitude shift keying (ASK), 2PSK, and QPSK, and the results showed the robustness of the algorithm against noise
Summary
With the recent advancements in telecommunication technologies, various bandwidth-critical applications have attracted the attention of users. Manual analysis becomes problematic and inaccurate when the number of intercepted (IF) time waveform, average and instantaneous spectrum of signal, sound, and signal and modulation types increases This method requires experienced analyzers and does not instantaneous amplitude. Manual analysis becomes problematic and inaccurate when the number of guarantee reliable classification results These shortcomings can be addressed via automatic intercepted modulation types increases. AMR is more powerful than MMR because it integrates an automatic does not guarantee reliable classification results These shortcomings can be addressed via modulation recognizer into an electronic receiver [2]. AMR is a challenging task, especially under noncooperative settings, because communication is a type of classical multiple-access channel, wherein users directly send information signal identification is difficult when the traits are unknown [5].
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