Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNet.

Zilong Wu, Hong Chen, Yingke Lei

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Transformer based HF communication demodulation.Scientific reports · 2026
Article

4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Zilong WuCollege of Electronic Countermeasures, National University of Defense Technology, Hefei 230037, China.ORCID 0000-0002-2931-5925

Hong ChenCollege of Electronic Countermeasures, National University of Defense Technology, Hefei 230037, China.

Yingke LeiCollege of Electronic Countermeasures, National University of Defense Technology, Hefei 230037, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This work improves a LeNet model algorithm based on a signal's bispectral features to recognize the communication behaviors of a non-collaborative short-wave radio station. At first, the mapping relationships between the burst waveforms and the communication behaviors of a radio station are analyzed. Then, bispectral features of simulated behavior signals are obtained as the input of the network. With regard to the recognition neural network, the structure of LeNet and the size of the convolutional kernel in LeNet are optimized. Finally, the five types of communication behavior are recognized by using the improved bispectral estimation matrix of signals and the ameliorated LeNet. The experimental results show that when the signal-to-noise ratio (SNR) values are 8, 10, or 15 dB, the recognition accuracy values of the improved algorithm reach 81.5%, 94.5%, and 99.3%, respectively. Compared with other algorithms, the training time cost and recognition accuracy of the proposed algorithm are lower and higher, respectively; thus, the proposed algorithm is of great practical value.

Indexed as

bispectrum estimationcommunication behaviorsconvolutional neural network (CNN)short-wave radio stationsignal recognition

Identifiers

PMID32756394

DOI10.3390/s20154320

PMCPMC7435670

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LicenceCC BY

Read underepoch 390

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.