Dual-Use of Modulation Recognition Techniques for Digital Communication Signals

Su, Wei; Kosinski, J.A.; Yu, Ming

doi:10.1109/lisat.2006.4302646

Cited by 19 publications

(10 citation statements)

References 26 publications

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“…Research has been conducted in classifying analog, digital, and OFDM modulation schemes [13]- [15] using waveform Fig. 4.…”

Section: Automatic Modulation Classificationmentioning

confidence: 99%

Framework of network centric signal sensing for automatic modulation classification

Kosinski

2010

2010 International Conference on Networking, Sensing and Control (ICNSC)

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This paper proposes a sensor network framework for sensing and exploiting unknown signals. It includes distributed decision fusion, distributed processing, centralized synchronous signal sensing, centralized asynchronous signal sensing, and system of systems single sensing. Data fusion is applied to enhance the SNR of weak signals and to eliminate channel distortion. Statistical estimation and modulation classification techniques are discussed.

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“…Research has been conducted in classifying analog, digital, and OFDM modulation schemes [13]- [15] using waveform Fig. 4.…”

Section: Automatic Modulation Classificationmentioning

confidence: 99%

Framework of network centric signal sensing for automatic modulation classification

Kosinski

2010

2010 International Conference on Networking, Sensing and Control (ICNSC)

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“…With the evolution of the modernistic communication technologies, the automatic recognition of modulation signal is an significant issue in fast-growing diversified applications like satellite communications, scientific probes, and military communications [1][2][3][4]. From former study consequences [5][6][7][8], automatic modulation classification (AMC) is an implement to identify unknown or partially known signals by applying a brief sequence of the signal.…”

Section: Introductionmentioning

confidence: 99%

A new method to solve the problem of facing less learning samples in signal modulation recognition

Liu

2020

J Wireless Com Network

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“…The IEEE P1990.6 standard (about CR) [1], reports that a credibility evaluation for modulation recognition of part of a civilian wireless signal sensing device has been considered as an output parameter. Reference [2] shows that the credibility evaluation has been regarded as an independent new operation of modulation recognition in military signal processing systems under conditions of noncooperation that can be used to judge an unknown signal. ratios under different hypotheses to measure the credibility.…”

Section: Introductionmentioning

confidence: 99%

Credibility Evaluation for Blind Processing Results of BPSK Signals by Using Phase Spectral Entropy

Wang¹,

Hu²

2016

International Journal on Smart Sensing and Intelligent Systems

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Abstract-A credibility test method based on the features of frequency domain entropy of a phase isproposed to evaluate the blind processing results of a BPSK signal. Initially, a reference signal was constructed depending on the certain identified modulation results. By analyzing the differences of the phase of the correlation series between the observed signal and the reference signal, a reliability test problem for the BPSK signal is performed by calculating the phase spectrum entropy and comparing it with a certain threshold. Simulation results show that the proposed method can be used to verify the reliability of the blind processing results of a BPSK signal at a low signal-to-noise ratio and without a priori knowledge of the signal parameters.

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Dual-Use of Modulation Recognition Techniques for Digital Communication Signals

Cited by 19 publications

References 26 publications

Framework of network centric signal sensing for automatic modulation classification

Framework of network centric signal sensing for automatic modulation classification

A new method to solve the problem of facing less learning samples in signal modulation recognition

Credibility Evaluation for Blind Processing Results of BPSK Signals by Using Phase Spectral Entropy

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