Modulation Recognition of Underwater Acoustic Communication Signals Based on Phase Diagram Entropy

Stanescu, Denis; Digulescu, Angela; Ioana, Cornel; Șerbănescu, Alexandru

doi:10.1109/oceans47191.2022.9977145

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Thus, using this approach, we can successfully characterize the modulation signals in order to realize their recognition process. The advantages of the characterization of single carrier modulations based on the phase diagram approach were already shown in (Scripcaru et al, 2020;Stanescu et al, 2022;Stanescu et al, 2023). In order to address the characterization of different modulation, the sliding windows are used and the PDE is computed on each window.…”

Section: Phase Diagram-based Entropy Approachmentioning

confidence: 99%

Spread spectrum modulation recognition based on phase diagram entropy

Stanescu

Digulescu

Ioana

et al. 2023

Front. Signal Process.

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Wireless communication technologies are undergoing intensive study and are experiencing accelerated progress which leads to a large increase in the number of end-users. Because of this, the radio spectrum has become more crowded than ever. These previously mentioned aspects lead to the urgent need for more reliable and intelligent communication systems that can improve the spectrum efficiency. Specifically, modulation scheme recognition occupies a crucial position in the civil and military application, especially with the emergence of Software Defined Radio (SDR). The modulation recognition is an indispensable task while performing spectrum sensing in Cognitive Radio (CR). Spread spectrum (SS) techniques represent the foundation for the design of Cognitive Radio systems. In this work, we propose a new method of characterization of Spread spectrum modulations capable of providing relevant information for the process of recognition of this type of modulations. Using the proposed approach, results higher than 90% are obtained in the modulation classification process, thus bringing an advantage over the classical methods, whose performance is below 75%.

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Section: Phase Diagram-based Entropy Approachmentioning

confidence: 99%

Spread spectrum modulation recognition based on phase diagram entropy

Stanescu

Digulescu

Ioana

et al. 2023

Front. Signal Process.

Self Cite

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“…Zhang et al [7] used machine learning algorithms to recognize modulation based on cumulant, power spectral density, instantaneous phase, instantaneous phase, and frequency characteristics. Denis Stanescu et al [8] used phase diagram entropy to characterize and identify various modulation types. Dai et al [9] carried out wavelet denoising and timefrequency feature extraction for the received signal and used the decision tree model for modulation recognition.…”

Section: Introductionmentioning

confidence: 99%

Modulation Recognition Method for Underwater Acoustic Communication Signals Based on Passive Time Reversal-Autoencoder with the Synchronous Signals

Bao

Sun

et al. 2023

Sensors

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In the process of the modulation recognition of underwater acoustic communication signals, the multipath effect seriously interferes with the signal characteristics, reducing modulation recognition accuracy. The existing methods passively improve the accuracy from the perspective of selecting appropriate signal features, lacking specialized preprocessing for suppressing multipath effects. So, the accuracy improvement of the designed modulation recognition models is limited, and the adaptability to environmental changes is poor. The method proposed in this paper actively utilizes common synchronous signals in underwater acoustic communication as detection signals to achieve passive time reversal without external signals and designs a passive time reversal-autoencoder to suppress multipath effects, enhance signals’ features, and improve modulation recognition accuracy and environmental adaptability. Firstly, synchronous signals are identified and estimated. Subsequently, a passive time reversal-autoencoder is designed to enhance power spectrum and square spectrum features. Finally, a modulation classification is performed using a convolutional neural network. The model is trained in simulation channels generated by Bellhop and tested in actual channels which are different from the training period. The average recognition accuracy of the six modulated signals is improved by 10% compared to existing passive modulation recognition methods, indicating good environmental adaptability as well.

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Modulation Recognition of Underwater Acoustic Communication Signals Based on Phase Diagram Entropy

Cited by 2 publications

References 7 publications

Spread spectrum modulation recognition based on phase diagram entropy

Spread spectrum modulation recognition based on phase diagram entropy

Modulation Recognition Method for Underwater Acoustic Communication Signals Based on Passive Time Reversal-Autoencoder with the Synchronous Signals

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