This letter deals with the compensation of the self-noise (jitter) in a nondata-aided (blind) feedforward symbol-timing estimator using two samples per symbol for linearly modulated waveforms transmitted through additive white Gaussian noise channels. The main contribution of this letter is the derivation of an appropriate prefilter to achieve nearly jitter-free digital blind feedforward timing recovery. Computer simulations illustrate that the proposed prefiltering scheme improves significantly the performance at mid and high signal-to-noise ratios.

By exploiting a general cyclostationary (CS) statistics-based framework, this letter develops a rigorous and unified asymptotic (large sample) performance analysis setup for a class of blind feedforward timing epoch estimators for linear modulations transmitted through time nonselective flat-fading channels. Within the proposed CS framework, it is shown that several estimators proposed in the literature can be asymptotically interpreted as maximum likelihood (ML) estimators applied on a (sub)set of the second- (and/or higher) order statistics of the received signal. The asymptotic variance of these ML estimators is established in closed-form expression and compared with the modified Crame/spl acute/r-Rao bound. It is shown that the timing estimator proposed by Oerder and Meyr achieves asymptotically the best performance in the class of estimators which exploit all the second-order statistics of the received signal, and its performance is insensitive to oversampling rates P as long as P/spl ges/3. Further, an asymptotically best consistent estimator, which achieves the lowest asymptotic variance among all the possible estimators that can be derived by exploiting jointly the second- and fourth-order statistics of the received signal, is also proposed.

This letter deals with the problem of nondata-aided carrier frequency offset estimation of noncircular modulations transmitted through unknown frequency-selective channels. By exploiting the unconjugated cyclostationary statistics induced by oversampling in the received waveform, an optimized carrier frequency offset estimator is proposed and its asymptotic (large sample) performance analyzed. In order for the proposed frequency estimator to achieve the minimum mean-square estimation error, it is shown that the oversampling rate need not be larger than twice the symbol rate. It is also shown that the proposed synchronizer is asymptotically jitter-free, admits a feedforward structure that may be implemented in digital form, and is suitable for burst transmissions.

Recently, S.J. Lee proposed a blind feedforward symbol timing estimator that exhibits low computational complexity and requires only two samples per symbol (see IEEE Commun. Lett., vol.6, p.205-7, 2002). We analyze Lee's estimator rigorously by exploiting efficiently the cyclostationary statistics present in the received oversampled signal; its asymptotic (large sample) bias and mean-square error (MSE) are derived in closed-form expression. A new blind feedforward timing estimator that requires only two samples per symbol and presents the same computational complexity as Lee's estimator is proposed. It is shown that the proposed new estimator is asymptotically unbiased and exhibits smaller MSE than Lee's estimator. Computer simulations are presented to illustrate the performance of the proposed new estimator with respect to Lee's estimator and existing conventional estimators.

The paper introduces and analyzes the asymptotic (large sample) performance of a family of blind feedforward nonlinear least-squares (NLS) estimators for joint estimation of carrier phase, frequency offset, and Doppler rate for burst-mode phase-shift keying transmissions. An optimal or "matched" nonlinear estimator that exhibits the smallest asymptotic variance within the family of envisaged blind NLS estimators is developed. The asymptotic variance of these estimators is established in closed-form expression and shown to approach the Cramer-Rao lower bound of an unmodulated carrier at medium and high signal-to-noise ratios (SNR). Monomial nonlinear estimators that do not depend on the SNR are also introduced and shown to perform similarly to the SNR-dependent matched nonlinear estimator. Computer simulations are presented to corroborate the theoretical performance analysis.

This paper introduces a family of blind feedforward nonlinear least-squares (NLS) estimators for joint estimation of the carrier phase and frequency offset of general quadrature amplitude modulated (QAM) transmissions. As an extension of the Viterbi and Viterbi (1983) estimator, a constellation-dependent optimal matched nonlinear estimator is derived such that its asymptotic (large sample) variance is minimized. A class of conventional monomial estimators is also proposed. The asymptotic performance of these estimators is established in closed-form expression and compared with the Cramer-Rao lower bound. A practical implementation of the optimal matched estimator, which is a computationally efficient approximation of the latter and exhibits negligible performance loss, is also derived. Finally, computer simulations are presented to corroborate the theoretical performance analysis and indicate that the proposed optimal matched nonlinear estimator improves significantly the performance of the classic fourth-power estimator.

This paper introduces a family of blind feedforward nonlinear estimators for joint estimation of carrier phase, frequency offset and Doppler rate for burst-mode phase shift keying (PSK) transmissions. An optimal or “matched” nonlinear estimator that exhibits the smallest asymptotic variance within the family of envisaged estimators is developed. The asymptotic performance of these estimators is established in closed-form expression and compared with the Cramèr-Rao lower bound for an unmodulated carrier. Finally, computer simulations are presented to corroborate the theoretical performance analysis.

This paper examines the asymptotic (large sample) performance of a family of non-data aided feedforward (NDA FF) nonlinear least-squares (NLS) type carrier frequency estimators for burst-mode phase shift keying (PSK) modulations transmitted through AWGN and flat Rician-fading channels. The asymptotic performance of these estimators is established in a closed-form expression and compared with the modified Cramer-Rao bound (MCRB). A best linear unbiased estimator (BLUE), which exhibits the lowest asymptotic variance within the family of NDA FF NLS-type estimators, is also proposed.

This letter proposes a class of blind feedforward carrier-phase estimators for higher order quadrature amplitude-modulated transmissions. As an extension of the Viterbi and Viterbi (V&V) estimator, a constellation-dependent optimal matched nonlinear estimator is derived such that its asymptotic variance is minimized. The asymptotic variances of the optimal matched and V&V estimators are established in closed-form expressions and compared. Computer simulations are presented to corroborate the theoretical performance analysis.

This paper proposes to analyze the performance of a family of non-data aided open-loop carrier frequency offset (FO) estimators for a linearly modulated signal transmitted through an unknown flat-fading (possibly frequency-selective) channel. The exact asymptotic (large sample) performance of these estimators is established and analyzed as a function of the received signal sampling frequency, signal-to-noise ratio (SNR), timing delay, and number of samples (N). It is shown that in the presence of timing errors, the performance of the estimators can be improved by oversampling (fractionally sampling) the received signal and by taking into account the entire cyclostationary information that is present in the received sequence.

By exploiting the received signal's second-order cyclostationary statistics, blind algorithms have been previously proposed for joint estimation of the frequency offset and the symbol timing delay of a linearly modulated waveform transmitted through a flat-fading channel. The goal of this paper is to establish and analyze the asymptotic (large sample) performance of the Gini-Giannakis (1998) and Ghogho-Swami-Durrani (1999) estimators as a function of the pulse shape bandwidth and the oversampling factor. It is shown that the performance of these estimators improves as the pulse shape bandwidth increases, and the best performance is obtained by selecting small values for the oversampling factor.