Identification the Modulation Type in Cognitive Radio Network Based on Alexnet Architecture

Mousa, Naseer A.; Sadkhan, Sattar B.

doi:10.1109/iiceta54559.2022.9888752

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Communication signal modulation recognition is based on the non-cooperative scenario between senders and receivers, which plays an important role in information recovery. In the field of wireless communication, modulation recognition is mostly based on in-phase and quadrature (IQ) samples [1][2][3], high-order Cumulant characteristics [4,5], signal instantaneous characteristics, and wavelet transforms characteristics [6], and then appropriate classifiers are designed to classify modulation types. However, these recognition methods are not suitable for complex and variable underwater acoustic channels.…”

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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