An Individual Communication Transmitter Fingerprint Feature Extraction Algorithm Based on Deep Learning

Aiming at solving the problem that communication transmitter fingerprint feature extraction cannot be operated effectively, an individual communication transmitter identification algorithm based on deep learning was proposed. Firstly the original communication transmitter signals were preprocessed (high-order analysis) to project to high dimensional feature space. Then a stacked auto-encoder was trained by unsupervised learning method through large amounts of unlabeled communication transmitter high dimensional samples. And on the basis of that, relatively small amounts of labeled communication transmitter samples was used to finetune the softmax regression model parameters under supervision. And thus, a deep learning network facing to communication transmitter fingerprint feature extraction model was designed. Identification experiments on real communication transmitter signal dataset proved the availability and effectiveness of proposed model.