A phase likelihood-based algorithm for blind identification of PSK signals

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Publication Type pre-print
School or College College of Engineering
Department Electrical & Computer Engineering
Creator Mathews, V. John
Other Author Zhu, Daimei; Detienne, David H.
Title A phase likelihood-based algorithm for blind identification of PSK signals
Date 2014-01-01
Description This paper presents a phase likelihood-based method for automatically identifying different phase-shift keying (PSK) modulations. This method identifies the PSK signals as the hypothesis for which the likelihood function of phase difference between nearby samples of the received signal is the maximum. This method does not need prior knowledge of carrier frequency or baud rate and can identify modulation types at relatively low signal-to-noise ratio (SNR) and using small number of input samples. Simulation results demonstrate that this algorithm can identify BPSK, QPSK and 8PSK signals with 100% accuracy with only 1000 symbols when the SNR of the input signal is better than 7 dB. Additional simulation results demonstrating the robustness of the algorithm to variations of the noise characteristics from the assumed Gaussian model are also included in the paper. Performance comparisons indicate that the approach of this paper can achieve 100% accuracy in modulation identification at 5-7 dB lower SNR than competing methods available in the literature.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
First Page 5730
Last Page 5740
Language eng
Bibliographic Citation Zhu, D., Mathews, V. J., & Detienne, D. H. (2014). A phase likelihood-based algorithm for blind identification of PSK signals. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 5730-4.
Rights Management (c) 2014 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Format Medium application/pdf
Format Extent 305,406 bytes
Identifier uspace,18888
ARK ark:/87278/s6r81qbn
Setname ir_uspace
Date Created 2014-08-26
Date Modified 2021-05-06
ID 712659
Reference URL https://collections.lib.utah.edu/ark:/87278/s6r81qbn
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