# Automatic Wireless Signal Classification: A Neural-Induced Support Vector Machine-Based Approach
###### Abstract  
Automatic Classification of Wireless Signals (ACWS), which is an intermediate step between signal detection and demodulation, is investigated in this paper. ACWS plays a crucial role in several military and non-military applications, by identifying interference sources and adversary attacks, to achieve efficient radio spectrum management. The performance of traditional feature-based (FB) classification approaches is limited due to their specific input feature set, which in turn results in poor generalization under unknown conditions. Therefore, in this paper, a novel feature-based classifier Neural-Induced Support Vector Machine (NSVM) is proposed, in which the features are learned automatically from raw input signals using Convolutional Neural Networks (CNN). The output of NSVM is given by a Gaussian Support Vector Machine (SVM), which takes the features learned by CNN as its input. The proposed scheme NSVM is trained as a single architecture, and in this way, it learns to minimize a margin-based loss instead of cross-entropy loss. The proposed scheme NSVM outperforms the traditional softmax-based CNN modulation classifier by managing faster convergence of accuracy and loss curves during training. Furthermore, the robustness of the NSVM classifier is verified by extensive simulation experiments under the presence of several non-ideal real-world channel impairments over a range of signal-to-noise ratio (SNR) values. The performance of NSVM is remarkable in classifying wireless signals, such as at low signal-to-noise ratio (SNR), the overall averaged classification accuracy is \(>97\%\) at SNR = \(-2\) dB and at higher SNR it achieves overall classification accuracy at \(>99\%\), when SNR = 10 dB. In addition to that, the analytical comparison with other studies shows the performance of NSVM is superior over a range of settings. Convolutional Neural Networks; Support Vector Machine; Automatic Classification of Wireless Signals; feature learning +
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achieving higher data rates by enabling adaptive transmission settings across varying channel conditions without any prior knowledge of the received signal [1]. In several military applications, it is used for surveillance of adversary attacks and electronic warfare purposes. Automatic Classification of Wireless Signals generally involves two steps: pre-processing of received signals and classification algorithm design. In the literature, ACWS techniques are broadly categorized into two main approaches: likelihood-based (LB) and feature-based (FB). Likelihood-based approaches are based on hypothesis testing, comparing the likelihood functions of received signals to classify different modulations by employing an average likelihood ratio test ALRT [2; 3; 4; 5], a generalized likelihood ratio test (GLRT) [2; 4; 3] and a hybrid likelihood ratio test (HLRT) [2; 3; 4]. These decision theoretic approaches achieve optimal performance, but at the cost of high computational complexity. Therefore, feature-based approaches are preferred in practice as suboptimal classifiers. In FB approaches, feature extraction is performed at a pre-processing unit followed-by a classification algorithm that classifies the received signal based on the features extracted at the pre-processing step. A general block-wise architecture of the feature-based classifier is given in Figure 1. In Reference [6], [PERSON] and [PERSON] proposed a decision tree-based classifier for automatic classification of digital and analogue modulation signals with spectral features, such as instantaneous amplitude, phase and frequency as its input feature vectors. [PERSON] and [PERSON] [7] proposed a hierarchal decision tree-based classifier with fourth order cumulants as their input features. In Reference [8], [PERSON] combines the instantaneous spectral features and fourth order cumulants of received signals as input features for a PSO-based support vector machine. The use of cyclostationay features for signal classification has been reported by Reference [9] in detail.