Blind Symbol Rate Estimation Research Articles

Non-Data-Aided Symbol Rate Estimation for a Low Roll-Off Factor Nyquist WDM Signal

Nyquist signals with low roll-off factors pose a challenge for non-data-aided blind symbol rate estimation problems in long-haul applications. We adopted the nonlinear least squares (NLS) approach to estimate the symbol rate of low roll-off factor signals. This method tolerates chromatic dispersion and additive Gaussian noise. In addition, this method can operate below two samples per symbol, thus enabling low-power-consumption receivers. Simulation and experimental results show that the mean squared error is below $10^{-4}$ with only 3000 samples needed for polarization-division-multiplexed (PDM) quadrature phase-shift keying (QPSK) or quadrature amplitude modulation (QAM) systems with a roll-off factor below 0.1 for an optical signal-to-noise ratio greater than 12 dB. Compared with the maximum likelihood estimation (MLE) method, our proposed method has a more robust performance under low roll-off factor signals and in low-sampling-rate scenarios. The mean squared error remains the same for a nominal symbol rate (i.e., the ratio of the symbol rate to the sampling rate, also widely known as oversampling rate) ranging from 0.1 to 0.9.

Blind symbol rate estimation based on BPF bank

Conventional real-time symbol rate estimation algorithm is based on the Fourier transform of the signal complex envelope. While this method is economic in resource consumption, its performance deteriorates at low signal-to-noise ratio (SNR), especially for the short burst signal applications. In this paper, we propose a novel method of using bandpass filter (BPF) bank to improve short burst signal's symbol rate estimation performance at low SNR or ratio of symbol energy to noise power spectral density (EsNo), equivalently. Simulation results show that our proposed algorithm can efficiently estimate symbol rate by using 200 symbols at 4 dB EsNo.

Blind symbol rate estimation using nonlinearity on sample correlation for digital coherent systems

We propose a symbol rate estimation technique by using absolute value of correlation of received signal for single-carrier polarization division multiplexed (PDM) systems in coherent detection system. We analytically show that spectrum of absolute value of correlation contains a peak indicating the symbol rate and has a robust performance which is insensitive to accumulated chromatic dispersion (CD) and first-order polarization-mode dispersion (PMD), amplified spontaneous emission (ASE) noise and laser frequency offset. Simulation result shows that this technique is applicable to long-haul application.

Periodic variation method for blind symbol rate estimation

SUMMARYIn order to obtain unknown symbol rate of incoming signal at a receiver, in this paper, cyclostationary features of linear digitally modulated signals are exploited by proposed periodic variation method. A low complexity but highly accurate symbol rate estimation technique is obtained. The proposed method is based on a superposed epoch analysis over autocorrelations obtained blindly in different sampling frequencies. The obtained autocorrelations are analyzed in the frequency domain, and it is seen that there are large oscillations when the autocorrelation is obtained around the symbol rate. Then, a superposed epoch analysis is developed in order to estimate symbol rate based of the periodic variations on the frequency responses of autocorrelations. The proposed algorithm is quite accurate in the noisy environment because the noise is having no frequency component after taking Fourier transform of autocorrelations in all sampling rates, and this feature is also valid for the offset frequency that the purposed estimation is not affected by offset frequency. Thus, a successful blind symbol rate estimation algorithm is obtained, and it performs much better error performance than those using the well‐known cyclic correlation based symbol rate estimations, as it is proven by the obtained performances presented in the paper. Copyright © 2013 John Wiley & Sons, Ltd.

A Blind Symbol Rate Estimation of Weak Signal Based on Cyclic Spectrum and Wavelet Transform

In the paper, we propose a new blind symbol rate estimation of weak signal with fixed sampling rate based on cyclic spectrum and wavelet transform. Firstly, the FFT characteristics is used to estimate the carrier frequency and the received signal is moved to the baseband by Digital Down Converter (DDC). Then, the wavelet transform is used for pre-estimating the symbol rate. Thirdly, the symbol rate estimation is proposed on the basis of cyclostationary theory and pre-estimated symbol rate. Simulation results verified the correctness of this method with MPSK signal. When the Signal to Noise Ratio (SNR) is −10 db, the approach proposed in this paper can accurately estimate the varied symbol rate with fixed sampling rate.

Blind symbol rate estimation of satellite communication signal by Haar wavelet transform

As the raised cosine shaping filter is often employed in practical satellite communication system, the envelope fluctuation at the symbol transition point is decreased which leads to the failure of the common wavelet algorithm under low SNR. Accordingly, a method of blind symbol rate estimation using signal preprocessing and Haar wavelet is proposed in this paper. Firstly, the effect of filter shaping can be reduced by the signal preprocessing. Then, the optimal scale factor is searched and the signal is processed and analyzed by the Haar wavelet transform. Finally, the symbol rate line is extracted and a nonlinear filter method is inducted for improving the estimation performance. Theoretical analysis and computer simulation show the efficiency of the proposed algorithm under low SNR and small roll-off factor.

Joint Blind Symbol Rate Estimation and Data Symbol Detection for Linearly Modulated Signals

This paper focuses on non-data aided estimation of the symbol rate and detecting the data symbols in linearlymodulated signals. A blind oversampling-based signal detector under the circumstance of unknown symbol period is proposed. First, the symbol rate is estimated using the Expectation Maximization (EM) algorithm. However, within the framework of EM algorithm, it is difficult to obtain a closed form for the loglikelihood function and the density function. Therefore, these two functions are approximated in this paper by using the Particle Filter (PF) technique. In addition, a symbol rate estimator that exploits the cyclic correlation information is proposed as an initialization estimator for the EM algorithm. Second, the blind data symbol detector based on the PF algorithm is designed.Since the signal is oversampled at the receiver side, a delayed multi-sampling PF detector is proposed to manage the intersymbol interference caused by oversampling, and to improve the demodulation performance of the data symbols. In the PF algorithm, the hybrid importance function is used to generate both data samples and channel model coefficients, and the Mixture Kalman Filter (MKF) algorithm is used to marginalize out the fading channel coefficients.

Asymptotic analysis of blind cyclic correlation-based symbol-rate estimators

This paper considers the problem of blind symbol rate estimation of signals linearly modulated by a sequence of unknown symbols. Oversampling the received signal generates cyclostationary statistics that are exploited to devise symbol-rate estimators by maximizing in the cyclic domain a (possibly weighted) sum of modulus squares of cyclic correlation estimates. Although quite natural, the asymptotic (large sample) performance of this estimator has not been studied rigorously. The consistency and asymptotic normality of this symbol-rate estimator is established when the number of samples N converges to infinity. It is shown that this estimator exhibits a fast convergence rate (proportional to N/sup -3/2/), and it admits a simple closed-form expression for its asymptotic variance. This asymptotic expression enables performance analysis of the rate estimator as a function of the number of estimated cyclic correlation coefficients and the weighting matrix. A justification for the high performance of the unweighted estimator in high signal-to-noise scenarios is also provided.