Demodulation of Chaos Phase Modulation Spread Spectrum Signals Using Machine Learning Methods and Its Evaluation for Underwater Acoustic Communication

• Published in: Sensors (Basel, Switzerland) Vol. 18; no. 12; p. 4217
• Main Authors: Li, Chao, Marzani, Franck, Yang, Fan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 01.12.2018; MDPI AG
• Subjects: chaos phase modulation sequence; Computer Science; direct sequence spread spectrum; Hardware Architecture; machine learning; partial least square regression; underwater acoustic communication; chaos phase modulation sequence; machine learning; partial least square regression; direct sequence spread spectrum; underwater acoustic communication; PLS; SEQUENCE; CLASSIFICATION; MAP; WIRELESS COMMUNICATION
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s18124217

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.