Wavelet transform and cyclic cumulant based modulation classification in wireless network

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.