Abstract
With the development of Internet of things, a large number of embedded devices are interconnected by ad hoc and wireless network. The embedded devices can work correctly, only by ensuring correct communication between them. Identifying modulation scheme is the precondition to ensure the correct communication between embedded devices. However, in the multipath channel, ensuring the correct communication between embedded devices is a great challenge. Multipath channel always exists in the wireless network. However, most of the available modulation classification algorithms are based on ideal channel. It leads to the low-modulation classification probability in multipath channel. To resolve this problem, we propose a novel modulation classification algorithm. The proposed algorithm can classify signal without prior information about multipath channel. We calculate feature by high-order cyclic cumulant and wavelet transform. The feature is robust to multipath channel. The simulation results show that the proposed algorithm can achieve the much better classification accuracy than the available method in multipath channel.
Highlights
With the development of Internet of things (IoT), a large number of embedded devices are interconnected by ad hoc and wireless networks.[1,2]
Modulation classification in multipath channel is the key technology of signal processing in wireless network
The classifier can identify ten modulation schemes, and rate of correct classification is over 96.5% when signal-to-noise ratio (SNR) is not lower than 3 dB
Summary
With the development of Internet of things (IoT), a large number of embedded devices are interconnected by ad hoc and wireless networks.[1,2] Every connected embedded device can communicate with each other. We can classify modulation schemes based on appropriate threshold.[22,23] Xu et al.[24] proposed an automatic modulation classification method based on likelihood function In their work, they studied the various classification solutions obtained by likelihood ratio test and discussed the detailed features related to all major algorithms. The proposed algorithm can avoid the multipath channel effect by combining wavelet transform and higher order cyclic cumulants. Compared with Fourier transform and shorttime Fourier transform, wavelet transform is based on the localization analysis of time and frequency.[44] Wavelet transform can multi-scale refine the signal by calculating flex and transition It can extract feature information of the signal effectively, and it can find the location and the amplitude of outliers.[45] wavelet transform is known as ‘‘mathematical microscope’’ for analyzing signals. If 8m 2 Z, Z represents integer fields, sn(i) 2 fs0(i), sÃ0(i) . . . , sk(i), sÃk(i)g satisfies the following equation
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