## 4 Overview of the Type of Classifiers Used for MR
### Ann  
ANN is a classifier approach commonly used in AMC. ANNs are motivated by the central nervous system and are widely used in PR and machine learning domain [90]. NNs include various layers, i.e., the input layer, the output layer, and different numbers of hidden layers in between. Each layer includes several nodes that are called neurons, which simulate human brain processing strategies. The nodes from previous layer are interlinked with corresponding nodes in the following layer on the basis of weights. Weights are responsible for mapping the input to the output [91]. Furthermore, ANN classifiers can be either supervised or unsupervised networks. The discrimination between the two networks is based on the prior existence of training data for learning purposes. In supervised ANNs, two data sets are used: training and test sets. In unsupervised networks, ANN clustering inputs the data into different clusters for training [92].  
Several problems exist in ANNs that affect their performance. ANNs have limited generalization capability at low SNR. In [43], the classification accuracy for ANN was enhanced to identify the digital modulation signal at low SNR. However, this method only covered lower-order digital modulation. In [76], the cyclic frequency domain and backpropagation neural network (BPNN) were used as input attribute and classifier, respectively. This method improved the accuracy at low SNR but required considerable training. In [93], a discrete wavelet NN group was used to classify the digital modulation types for improving higher-order recognition. In this method, a single NN was distributed into multiple ANNs, which increased the complexity of the algorithm. Optimization techniques for enhancing the learning process and reducing the complexity are reported to be helpful. In [72; 94], principal component analysis (PCA) was used to reduce the complexity of ANNs and improve the classification accuracy at low SNR. BPNN was used in conjunction with reduced feature vectors through PCA to reduce training time and computational complexity and obtain higher-order MR at low SNR. In [57], the artificial bee colony (ABC) technique was used to improve the performance of ANNs in multipath fading channels. ABC was used to find the optimal weight of ANNs. However, this method required high SNR to obtain good accuracy.  
An interesting work on deep neural network (DNN)-based AMC was reported in [95], which used a DNN structure with three hidden layers. Twenty-one features from data samples were fed to the classifier. The proposed classifier produced the probabilities of each modulation class at eachnode by utilizing a Soft Max layer at the output layer. The proposed model was evaluated for AMC under AWGN and Rican channel environments by considering Doppler's frequencies and SNR range. The results showed that 90% classification accuracy was achieved.  
A recent development was the use of deep DNN to identify the signals in fiber-optic network. The algorithm used by [96] starts by applying the constant modulus algorithm (CMA) equalization then finding the signals amplitude histograms (AH) and feeding the data to the DNN. The non-data-aided (NDA) modulation format identification (MFI) was used for identifying three types of modulation techniques and achieved a 100% accuracy over a wide range of optical signal to noise ratio (OSNR).  
The authors of [97] proposed a deep learning (DL)-based intelligent constellation analyzer that could realize modulation recognition as well as OSNR estimation. They utilized CNN as DL models and treat constellation map as original constellation distribution without knowing any other parameters of the M-PSK and M-QAM signals to be identified.