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DCGAN based spectrum sensing data enhancement for behavior recognition in self-organized communication network

Communication behavior recognition is an issue with increasingly importance in the anti-terrorism and national defense area. However, the sensing data obtained in actual environment is often not sufficient to accurately analyze the communication behavior. Traditional means can hardly utilize the scarce and crude spectrum sensing data captured in a real scene. Thus, communication behavior recognition using raw sensing data under small-sample condition has become a new challenge. In this paper, a data enhanced communication behavior recognition (DECBR) scheme is proposed to meet this challenge. Firstly, a preprocessing method is designed to make the raw spectrum data suitable for the proposed scheme. Then, an adaptive convolutional neural network structure is exploited to carry out communication behavior recognition. Moreover, DCGAN is applied to support data enhancement, which realize communication behavior recognition under small-sample condition. Finally, the scheme is verified by experiments under different data size. The results show that the DECBR scheme can greatly improve the accuracy and efficiency of behavior recognition under small-sample condition.