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Proceedings ArticleDOI

Symbol rate estimation by the wavelet transform

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TLDR
In this article, the authors used wavelet transform to locate the transients produced from phase changes to estimate the symbol rate of an M-ary phase shift keying (PSK) signal.
Abstract
Demodulation of a digital modulated waveform requires the symbol rate of a received signal. This parameter may not be known a priori and may need to be estimated in the receiver. This paper studies the use of wavelet transform to estimate the symbol rate of an M-ary phase shift keying (PSK) signal. The idea is to use wavelet transform to locate the transients produced from phase changes. The separation between transients gives a symbol rate estimate. Previous work uses the transform coefficients in a single scale to estimate symbol rate. This paper improves the performance of the estimator by combining the coefficients at several scales before estimation. The accuracy of the estimator is shown to be within 3-6dB of the CRLB, when the sampling rate is four times the carrier frequency and the carrier-to-noise ratio is greater than 7 dB.

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Citations
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Analog to Digital Cognitive Radio: Sampling, Detection and Hardware

TL;DR: In this article, the authors proposed to recover second-order statistics from the low rate samples, rather than the signal itself, to cope with low signal-to-noise ratios.
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Analog-to-Digital Cognitive Radio: Sampling, Detection, and Hardware

TL;DR: The radio spectrum is the radio-frequency portion of the electromagnetic spectrum, and most of the frequency bands are already allocated to one or more PUs, so new users cannot easily find free frequency bands.
Proceedings ArticleDOI

Modulation classification of communication signals

TL;DR: A novel algorithm using wavelet transform and pattern recognition to identify the modulation types of the communication signals automatically and is efficient at the SNR /spl les/ 15 dB.
Proceedings ArticleDOI

The improvement of symbol rate estimation by the wavelet transform

TL;DR: An improved version of the symbol rate estimator by the wavelet transform using the WT magnitudes of the baseband modulated signal instead of those of the MF signals is proposed, which remarkably improves the anti-noise ability of the estimator.
Proceedings ArticleDOI

An automatic digital modulation classifier for measurement on telecommunication networks

TL;DR: In this paper, a method for automatic classification of digital modulations, without any knowledge of the signal parameters, is presented, which can recognize classical single-carrier modulations such as M-PSK (M-ary phase shift keying), M-FSK (m-ary frequency-shift keying) and M-QAM (mary quadrature amplitude modulation) used for ADSL and VDSL (very high speed DSL) standards.
References
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Journal ArticleDOI

Signal classification using statistical moments

TL;DR: An automatic modulation classification algorithm utilizing the statistical moments of the signal phase is developed and used to classify the modulation type of general M-ary PSK signals and is compared with the quasi-log-likelihood radio, square-law, and phase-based classifiers.
Journal ArticleDOI

Likelihood methods for MPSK modulation classification

TL;DR: The performance of a single-term approximation to the optimal LF classifier is evaluated analytically and is shown to be very close to that of the optimal.
Journal ArticleDOI

The use of the wavelet transform in the detection of an unknown transient signal

TL;DR: It is shown that prior information regarding the relative bandwidth and the time-bandwidth-product of the signal to be detected is efficiently incorporated into the detection problem formulation, and the proposed detection scheme is most suitable for detection of unknown transient signals.
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