Automatic Wireless Signal Classification: A Neural-Induced Support Vector Machine-Based Approach

Automatic Wireless Signal Classification: A Neural-Induced Support Vector Machine-Based Approach

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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, crystal beaded candle holder 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 neflintw-r6mpw 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.