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This paper tackles the problem of the signal‐to‐noise ratio estimation for cyclic prefix (CP)‐Less orthogonal frequency division multiplexing (OFDM) system in the presence of Gaussian noise over multipath fading channels. First, we introduce the cyclic correlation of the CP‐Less OFDM signal with CP, and analyze the cyclic correlations for Gaussian noise and received signal. Second, we isolate the Gaussian noise power from the signal power. Finally, we obtain the close form expression of SNR estimation by using cyclic correlations. Computer simulation results verify the effectiveness of the proposed method. Key Points: A cyclic prefix (CP)‐less orthogonal frequency division multiplexing system is presentedAlignment signals are designed to eliminate inter symbol interference caused insufficient CP lengthCyclic correlation of CP is used to estimate signal‐to‐noise ratio [ABSTRACT FROM AUTHOR]

A novel technique based on cubic spline interpolation with linear least square regression (CSILLSR) is developed to calculate the signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images. The SNR from CSILLSR method is compared with methods of nearest, linear interpolation, a combination of linear interpolation and nearest, nonlinear least square regression, autocorrelation Levinson- Durbin recursion, and adaptive slope nearest neighbourhood. Samples of SEM images with various accelerating voltages, beam diameters, surface tilts and contrast were applied to evaluate the performance of CSILLSR method in terms of SNR values of the SEM images. The new method is able to generate more accuracy results than the other six methods. In addition, the CSILLSR Wiener filter appears to be the best filter to reduce and remove white Gaussian noise from SEM images as compared to the average filter and median filter. [ABSTRACT FROM AUTHOR]

Signal-to-noise ratio (SNR) estimation available in the literature are designed based on the assumption of Gaussian noise models. These estimators may produce misleading results when the distribution of the noise deviates from Gaussian. This paper investigates the performance of existing SNR estimators in an additive non-Gaussian noise channel based on a Gaussian mixture model. An expectation-maximization (EM) based approach is proposed for optimum SNR estimation in the non-Gaussian noise channel. In addition, the Cramer-Rao bound is derived and used as a benchmark to assess the performance of the SNR estimators. Simulation results confirm the optimality and robustness of the proposed EM-based estimator in Gaussian and non-Gaussian noise channels. [ABSTRACT FROM AUTHOR]

White Gaussian noise (WGN) commonly exists in acquisition and transmission of heart sound (HS) signals. The energy distributions of WGN and HS in wavelet decomposition levels (WDLs) are explored. The statistical analysis indicates that for WGN, energy proportions of detail portions of WDLs and energy proportion of the 2nd WDL are fixed. This finding is verified by using Monte Carlo test. Moreover, for a HS signal recorded under sampling frequency of 4 kHz, energies of the 1st and 2nd WDL are almost the same, which are validated by theoretical analysis and practical observation on three HS benchmark datasets. Based on these findings, equations estimating WGN energy and signal to noise ratio (SNR) for a noisy HS signal are created. In addition, a novel energy distribution-based wavelet multi-threshold de-noising approach (ED-WMTD) is proposed to reduce WGN. In which, firstly based on the energy distribution in WDLs and estimated energy of WGN, WGN energy in detail portion of each WDL is figured out. Then, soft-threshold method is adopted. The best threshold in a WDL is defined as the one by which the energy loss of noisy HS signal in this WDL is most similar to the energy of WGN in detail portion of this WDL. The accuracy of such a WGN energy estimation method is evaluated by average error of SNR estimation. ED-WMTD is assessed using mean square error and compared with four generally used WMTD methods. Experimental results show that this novel HS de-noising approach not only filters out HS noise effectively but also well retains its pathological information. [ABSTRACT FROM AUTHOR]

Signal to noise ratio (SNR) estimators are required for many radio engineering applications. In this paper, a SNR estimator based on the first and second order moments is derived and examined for constant envelope modulations over additive white Gaussian noise (AWGN) channel. Firstly, a modification method is proposed to reduce the bias of conventional first and second order moments based SNR estimator. Then, in order to reduce hardware implementation complexity, multi segments of cubic polynomial are utilized to approximate the nonlinear inverse function of the proposed SNR estimator. The approximate expression is very precise in the SNR range from -10dB to 20 dB as illustrated by numerical simulation results. Besides, practical hardware circuit is proposed for FPGA implementation. Simulation results show that the proposed SNR estimator has the lower normalized bias and variance when compared with two other classic estimators. [ABSTRACT FROM AUTHOR]

This correspondence deals with the problem of estimating average signal-to-noise ratio (SNR) for a communication link employing binary frequency shift keying (BFSK) in the presence of a carrier frequency offset (CFO). The transmitted symbols are corrupted by Rayleigh fading and additive white Gaussian noise (AWGN). We treat the CFO as a nuisance parameter and estimate it using a data statistics based estimator. This estimate is then used to design a maximum likelihood (ML) estimator to get the estimates of SNR. We also derive the Cramér–Rao bound (CRB) for the estimators and have shown the performance of both the data-aided and non-data-aided estimators. [ABSTRACT FROM PUBLISHER]

We propose a new SNR estimation method based on the preamble for OFDM systems in frequency selective channels. The OFDM training symbols in the preamble are equalized by the known data in frequency domain and employed to estimate the noise variance. The second order moments of the received symbols are used to estimate the signal plus noise power in the OFDM packets. The SNRs on the subchannels and the average SNR of the packets can all be estimated. Simulation results show that the proposed method is robust to frequency selectivity in wireless channels, and its performance is considerably improved compared with the available methods. [ABSTRACT FROM AUTHOR]

This paper considers how to estimate the average signal-to-noise ratio (SNR) for a communication system employing orthogonal non-coherent M-ARY frequency shift keying (NCMFSK), in white Gaussian noise (AWGN) and over both symbol-by-symbol fading channels and block fading channels. The proposed algorithm finds its application in a variety of applications including a cooperative transmission system, which is the main motivation behind this study. The maximum likelihood estimator and one using data statistics have been derived and simulated for various scenarios including data-aided, non-data aided and joint estimation using both the data and pilot sequences. We also derive the Cramer-Rao bound for the estimators in the case of Rayleigh fading channels. The results show that for a particular region of interest (e.g. high SNR or low SNR) and depending upon the availability of pilot sequence, a particular SNR estimation scheme is suitable. [ABSTRACT FROM AUTHOR]

In this paper, we derive for the first time analytical expressions for the exact Cramer-Rao lower bounds on the variance of unbiased non-data-aided (NDA) signal-to-noise ratio (SNR) estimators of square QAM-modulated signals. The channel is assumed to be constant over the observation interval and the received signal is supposed to be corrupted by additive white Gaussian noise (AWGN). The derived expressions corroborate previous attempts to numerically compute the considered CRLBs. It will be shown that the NDA CRLBs differ widely from one modulation order to another especially at moderate SNR levels. [ABSTRACT FROM PUBLISHER]