Automatic Modulation Classification for Underwater Acoustic Communication Signals Based on Deep Complex Networks
Open Access
- 9 February 2023
- Vol. 25 (2), 318
- https://doi.org/10.3390/e25020318
Abstract
Automatic modulation classification (AMC) is an important method for monitoring and identifying any underwater communication interference. Since the underwater acoustic communication scenario is full of multi-path fading and ocean ambient noise (OAN), coupled with the application of modern communication technology, which is usually susceptible to environmental influences, automatic modulation classification (AMC) becomes particularly difficult when it comes to an underwater scenario. Motivated by the deep complex networks (DCN), which have an innate ability to process complex data, we explore DCN for AMC of underwater acoustic communication signals. To integrate the signal processing method with deep learning and overcome the influences of underwater acoustic channels, we propose two complex physical signal processing layers based on DCN. The proposed layers include a deep complex matched filter (DCMF) and deep complex channel equalizer (DCCE), which are designed to remove noise and reduce the influence of multi-path fading for the received signals, respectively. Hierarchical DCN is constructed using the proposed method to achieve better performance of AMC. The influence of the real-world underwater acoustic communication scenario is taken into account; two underwater acoustic multi-path fading channels are conducted using the real-world ocean observation dataset, white Gaussian noise, and real-world OAN are used as the additive noise, respectively. Contrastive experiments show that the AMC based on DCN can achieve better performance than the traditional deep neural network based on real value (the average accuracy of the DCN is 5.3% higher than real-valued DNN). The proposed method based on DCN can effectively reduce the influence of underwater acoustic channels and improve the AMC performance in different underwater acoustic channels. The performance of the proposed method was verified on the real-world dataset. In the underwater acoustic channels, the proposed method outperforms a series of advanced AMC method.Funding Information
- National Natural Science Foundation of China (52171339)
This publication has 42 references indexed in Scilit:
- Going deeper with convolutionsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine TranslationPublished by Association for Computational Linguistics (ACL) ,2014
- Hierarchical Blind Modulation Classification for Underwater Acoustic Communication Signal via Cyclostationary and Maximal Likelihood AnalysisPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2013
- Modulation detection of underwater acoustic communication signals through cyclostationary analysisPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- Generalization Characteristics of Complex-Valued Feedforward Neural Networks in Relation to Signal CoherenceIEEE Transactions on Neural Networks and Learning Systems, 2012
- A Fast Learning Algorithm for Deep Belief NetsNeural Computation, 2006
- Long Short-Term MemoryNeural Computation, 1997
- Automatic identification of digital modulation typesSignal Processing, 1995
- Support-vector networksMachine Learning, 1995
- A survey of decision tree classifier methodologyIEEE Transactions on Systems, Man, and Cybernetics, 1991