Aesthetics

Programming language

Window (computing)

Fast Fourier transform

Philosophy

Time–frequency analysis

Artificial intelligence

Frequency domain

Telecommunications

Wireless

Cognitive radio

SIGNAL (programming language)

Operating system

Computer science

Time domain

Algorithm

Modulation (music)

Real-time computing

Sliding window protocol

Bandwidth (computing)

Computer vision

Radar

IEEE Internet of Things Journal

With the development of the Internet of Things (IoT), the IoT devices are increasing day by day, resulting in increasingly scarce spectrum resources. At the same time, many IoT devices are facing inevitable malicious attacks. The cognitive Radio-enabled IoT (CR-IoT) is proposed as an effective method for spectrum resource allocation and risk monitoring in the IoT. The signal detection and modulation recognition are the key technologies for CR-IoT, addressing the problem of multisignal detection and automatic modulation classification (AMC) is one of the prerequisites for realizing secure dynamic spectrum access. Based on sliding window and deep learning (DL), this study proposes a multisignal frequency domain detection and recognition method. The frequency spectrum of the time-domain overlapping signal is obtained through the fast Fourier transform (FFT), and the frequency spectrum is segmented based on the signal energy detection method. Finally a complex convolutional neural network (CNN) is constructed for the identification of signal spectrum information. The proposed method can recognize 264 time-domain aliasing and frequency-closed signals with an accuracy of 97.3% under the influence of −2 dB corresponding to the noise of the calibration signal. In addition, the proposed method eliminates the influence of bandwidth, which can effectively detect and recognize the signal types of each component in the frequency band. This method has wide applicability and provides an effective scheme for the IoT cognitive technology.

Multisignal Modulation Classification Using Sliding Window Detection and Complex Convolutional Network in Frequency Domain