Modulation recognition in maritime multipath channels: A blind equalization-aided deep learning approach

Modulation recognition has been long investigated in the literature, however, the performance could be severely degraded in multipath fading channels especially for high-order Quadrature Amplitude Modulation (QAM) signals. This could be a critical problem in the broadband maritime wireless communications, where various propagation paths with large differences in the time of arrival are very likely to exist. Specifically, multiple paths may stem from the direct path, the reflection paths from the rough sea surface, and the refraction paths from the atmospheric duct, respectively. To address this issue, we propose a novel blind equalization-aided deep learning (DL) approach to recognize QAM signals in the presence of multipath propagation. The proposed approach consists of two modules: A blind equalization module and a subsequent DL network which employs the structure of ResNet. With predefined searching step-sizes for the blind equalization algorithm, which are designed according to the set of modulation formats of interest, the DL network is trained and tested over various multipath channel parameter settings. It is shown that as compared to the conventional DL approaches without equalization, the proposed method can achieve an improvement in the recognition accuracy up to 30% in severe multipath scenarios, especially in the high SNR regime. Moreover, it efficiently reduces the number of training data that is required.