Editing Signal modulation (section)

===Automatic digital modulation recognition (ADMR)===
Automatic digital modulation recognition in intelligent communication systems is one of the most important issues in [[software-defined radio]] and [[cognitive radio]]. According to incremental expanse of intelligent receivers, automatic modulation recognition becomes a challenging topic in telecommunication systems and computer engineering. Such systems have many civil and military applications. Moreover, blind recognition of modulation type is an important problem in commercial systems, especially in [[software-defined radio]]. Usually in such systems, there are some extra information for system configuration, but considering blind approaches in intelligent receivers, we can reduce information overload and increase transmission performance. Obviously, with no knowledge of the transmitted data and many unknown parameters at the receiver, such as the signal power, carrier frequency and phase offsets, timing information, etc., blind identification of the modulation is made fairly difficult. This becomes even more challenging in real-world scenarios with multipath fading, frequency-selective and time-varying channels.<ref>
{{cite journal
 | title = Survey of automatic modulation classification techniques: classical approaches and new trends
 | author = Dobre, Octavia A., Ali Abdi, Yeheskel Bar-Ness, and Wei Su. Communications, IET 1, no. 2 (2007): 137–156.
 | journal= IET Communications
 | volume = 1
 | issue = 2
 | year = 2007
 | pages = 137–156
 | url = http://web.njit.edu/~abdi/IEE_COM0176_WithFigures.pdf
 | doi = 10.1049/iet-com:20050176
 }}</ref>

There are two main approaches to automatic modulation recognition. The first approach uses likelihood-based methods to assign an input signal to a proper class. Another recent approach is based on feature extraction.