According to good classification by the correct rate of a neural network classifier, a multilayer perceptron (MLP) classifier with better generalization, as well as, addition of time-frequency features set for classifying six difierent modulation types has been proposed.
The activation of CB1 in various cell types has been coupled to ion channel modulation [44] [45].
Recently Hardy type modulation spaces have been proposed in [13].
There are a great many ways through which signal modulation can be done, and each type has its advantages and disadvantages.
One of the popular methods in the literature has been the use of maximum likelihood (ML) for modulation types.
We have shown that the proposed algorithm is robust because it does not require the estimation of error probability, is insensitive to the high order of Galois field, and has the best detection performances for the most of modulation types.
Most studies of modulation coding, however, have employed one modulation type in stationary carriers, and in cases where mixed-modulation sounds have been used, the FM component has typically been confined to an extremely narrow range within a critical band.
Only few instances for this type of modulation have been reported.
All these modulation types are used in serial transfer mode.
Jondral [12] proposes a modulation classifier utilizing the pattern recognition approach for recognition of both analog and digital modulation types.
