Abstract⎯Signal-to-noise ratio (SNR) estimation for signal which can be modeled by Auto-regressive (AR) process is studied in this paper. First, the conventional frequency domain method is introduced to estimate the SNR for the received signal in additive white Gauss noise (AWGN) channel. Then a parametric SNR estimation algorithm is proposed by taking advantage of the AR model information of the received signal. The simulation results show that the proposed parametric method has better performance than the conventional frequency domain method in case of AWGN channel. Index Terms⎯Auto-regressive model, AWGN channel, model information, SNR (Signal-to-noise ratio) estimation
International audienceIn this paper, we introduce new parametric generative driven auto-regressive (...
Signal modeling is concerned with the representation of signals. The modeled signal consists of par...
In estimating the linear prediction coefficients for an autoregressive spectral model, the concept o...
Non-data-aided (NDA) parameter estimation is consideredfor binary-phase-shift-keying transmission in...
Signal-to-noise ratio (SNR) estimation is an important parameter that is required in any receiver or...
SNR estimation has been studied extensively in the past. Nevertheless, vast majority of prior works ...
A frequency domain approach to continuous-time auto regressive (AR) signal modeling is proposed. The...
Signal to noise ratio (SNR) estimators are required for many radio engineering applications. In this...
In this work, first the Cramer-Rao lower bound (CRLB) of the signal-to-noise ratio (SNR) estimate fo...
This paper considers the problem of estimating the parameters of an autoregressive (AR) process in p...
A method by which a popular signal-to-noise ratio (SNR) estimator can be re-configured to yield impr...
Signal-to-noise ratio (SNR) estimation available in the literature are designed based on the assumpt...
The performance of speech enhancement algorithms to a large extent is related to the employed signal...
This paper describes an efficient model to describe an autoregressive (AR) signal with slowly-varyin...
A method for autoregressive (AR) modeling of stationary stochastic signals has been proposed based o...
International audienceIn this paper, we introduce new parametric generative driven auto-regressive (...
Signal modeling is concerned with the representation of signals. The modeled signal consists of par...
In estimating the linear prediction coefficients for an autoregressive spectral model, the concept o...
Non-data-aided (NDA) parameter estimation is consideredfor binary-phase-shift-keying transmission in...
Signal-to-noise ratio (SNR) estimation is an important parameter that is required in any receiver or...
SNR estimation has been studied extensively in the past. Nevertheless, vast majority of prior works ...
A frequency domain approach to continuous-time auto regressive (AR) signal modeling is proposed. The...
Signal to noise ratio (SNR) estimators are required for many radio engineering applications. In this...
In this work, first the Cramer-Rao lower bound (CRLB) of the signal-to-noise ratio (SNR) estimate fo...
This paper considers the problem of estimating the parameters of an autoregressive (AR) process in p...
A method by which a popular signal-to-noise ratio (SNR) estimator can be re-configured to yield impr...
Signal-to-noise ratio (SNR) estimation available in the literature are designed based on the assumpt...
The performance of speech enhancement algorithms to a large extent is related to the employed signal...
This paper describes an efficient model to describe an autoregressive (AR) signal with slowly-varyin...
A method for autoregressive (AR) modeling of stationary stochastic signals has been proposed based o...
International audienceIn this paper, we introduce new parametric generative driven auto-regressive (...
Signal modeling is concerned with the representation of signals. The modeled signal consists of par...
In estimating the linear prediction coefficients for an autoregressive spectral model, the concept o...