Demodulation of Chaos Phase Modulation Spread Spectrum Signals Using Machine Learning Methods and Its Evaluation for Underwater Acoustic Communication.
Fiche publication
Date publication
décembre 2018
Journal
Sensors (Basel, Switzerland)
Auteurs
Membres identifiés du Cancéropôle Est :
Pr MARZANI Franck
Tous les auteurs :
Li C, Marzani F, Yang F
Lien Pubmed
Résumé
The chaos phase modulation sequences consist of complex sequences with a constant envelope, which has recently been used for direct-sequence spread spectrum underwater acoustic communication. It is considered an ideal spreading code for its benefits in terms of large code resource quantity, nice correlation characteristics and high security. However, demodulating this underwater communication signal is a challenging job due to complex underwater environments. This paper addresses this problem as a target classification task and conceives a machine learning-based demodulation scheme. The proposed solution is implemented and optimized on a multi-core center processing unit (CPU) platform, then evaluated with replay simulation datasets. In the experiments, time variation, multi-path effect, propagation loss and random noise were considered as distortions. According to the results, compared to the reference algorithms, our method has greater reliability with better temporal efficiency performance.
Mots clés
chaos phase modulation sequence, direct sequence spread spectrum, machine learning, partial least square regression, underwater acoustic communication
Référence
Sensors (Basel). 2018 Dec 1;18(12):