# Automatic Wireless Signal Classification: A Neural-Induced Support Vector Machine-Based Approach
###### Abstract
Machine Learning classifiers such as decision tree (DT) [10], Support Vector Machine (SVM) [11], and k-nearest neighbor (KNN) [12] have been used widely as shallow classifiers to classify wireless signals based-on the aforementioned feature set. However, these conventional FB approaches mainly rely on expert knowledge which may perform well on the proposed solution but suffers from a lack of generality and time-consumption with high computational complexity. This is due to their primary dependence on handcrafted specific features, which may not be suitable for different modulation sets and various adaptive channel conditions. To obviate manual feature engineering, deep learning (DL) [13], which is a sub-field of machine learning, has been used in several studies. A DL model has the ability to learn the features automatically without relying on any mathematical model or expert knowledge. A fully connected neural network and the hyperparameters can be tuned by conducting engineering experiments over the underlying task. Deep learning [14] has shown remarkable performance in several fields (not limited to), such as image classification [15; 16; 17], natural language processing (NLP) [18] and drug discovery [19; 20]. Recently, these deep learning-based approaches such as Convolutional Neural Networks (CNNs) [21; 22], stack autoencoders [23] and deep belief networks [24] have been applied for modulation classification as well as performing physical layer low-level tasks in wireless communication. Since these deep learning-based methods enable the receiver to learn different features automatically from the input received signal latent space, in several studies, neural networks have been employed as decision classifiers for a pre-processed received signal. For example, [PERSON] and [PERSON], in Reference [6], used an artificial neural network (ANN) with a genetic algorithm (GA) for automatic modulation classification. In their study, the GA was used to select the best features from a statistical and spectral  
Figure 1: A simple block-wise architecture of a Feature-Based (FB) classifier. feature set. In Reference [25], cumulants are used for classification features with Convolutional Neural Networks (CNN). [PERSON] [21] used CNN directly for modulation classification and achieved a promising performance compared to previous feature-based neural network approaches. In Reference [26], CNN was used to learn features separately, which are then used as input for a Support Vector Machine classifier. However, all of the aforementioned methods based on Convolutional Neural Networks (CNN) used the softmax activation function (multinomial logistic regression) for the classification decision. An alternative to softmax is the Support Vector Machine (SVM), which has been applied to several classification tasks [27]. The use of SVMs in combination with Convolutional Neural Networks (CNN) have been proposed in the past as part of a multistage process, mainly for image classification [28]. In which, a deep Convolutional Neural Network was first trained using supervised objectives to learn good invariant hidden latent representations. Then, these corresponding hidden features of data samples are treated as input to SVMs [29]. This technique usually improves the performance slightly, but in such settings, the low-level features have not been fine-tuned with respect to the Support Vector Machine's objective. Therefore, in this paper, a novel feature-based algorithm Neural-Induced Support Vector Machine (NSVM) is proposed, in which we replace the softmax layer of the CNN model with a SVM. The objective function of the Support Vector Machine (SVM) is rewritten to train CNN and SVM as a combined architecture. In this way, the proposed scheme learns to minimize margin loss rather than cross-entropy loss. The proposed schemes show remarkable improvement in the performance, as compared to the traditional CNN-based model for classification of wireless signals. The remainder of the paper is organized as follows: Section 2 introduces the system model.