Blind identification of QAM signals using a likelihood-based algorithm

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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
Title Blind identification of QAM signals using a likelihood-based algorithm
Date 2013-01-01
Description This paper presents a method for automatically identifying different QAM modulations. This method identifies the modulation type as the hypothesis for which the likelihood function of the amplitudes of the received signal is the maximum. The derivation of the likelihood functions assumes additive white Gaussian noise and no pulse shaping. In order to accommodate pulse shaping in the received signal, the system sub-samples the incoming signals non-uniformly so that the distribution of the amplitudes of the sub-sampled signals approximately matches that of QAM signals without pulse shaping. This method does not need prior knowledge of carrier frequency and baud rate and can identify modulation types at relatively low SNRs and with relatively few symbols. Simulation results demonstrating accurate modulation identification in the presence of additive noise are included in the paper. Results presented in the paper with non-Gaussian noise indicate that the system is robust to variations from the assumed noise model.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
First Page 158
Last Page 163
Language eng
Bibliographic Citation Zhu, D., & Mathews, V. J. (2013). Blind identification of QAM signals using a likelihood-based algorithm. 2013 IEEE Digital Signal Processing and Signal Processing Education Meeting, DSP/SPE 2013 - Proceedings, art. no. 6642583, 158-63.
Rights Management (c) 2013 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 648,482 bytes
Identifier uspace,18339
ARK ark:/87278/s6fv1vzf
Setname ir_uspace
Date Created 2014-02-20
Date Modified 2021-05-06
ID 711297
Reference URL https://collections.lib.utah.edu/ark:/87278/s6fv1vzf
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