Non-data-aided signal-to-noise-ratio estimation

Wiesel, Ami; Goldberg, J.; Messer, H.

doi:10.1109/icc.2002.996844

Cited by 85 publications

(48 citation statements)

References 9 publications

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“…On the other hand, the estimation of gain and SNR must be performed on a per-frame basis, since in the ACM mode two consecutive frames might carry different MODCODs formats. The problem of gain and SNR estimation has been widely studied in the past (for example, see [16]- [18]). However the application of these classical techniques to the DVB-S2 scenario raise an important issue: the presence of a strong phase noise.…”

Section: A Gain and Snr Estimation Algorithmmentioning

confidence: 99%

On Pilot-Symbol-Assisted Carrier Synchronization for DVB-S2 Systems

Barbieri

Colavolpe

2007

IEEE Trans. on Broadcast.

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Abstract-We consider the problem of carrier synchronization in future 2nd-generation satellite digital video broadcasting (DVB-S2) receivers. In this scenario, this task is made harder by the complexity constraints, related to the use of consumer-grade equipment. Making use of the distributed pilot symbols of the DVB-S2 standard, low-complexity techniques for fine frequency estimation and for detection in the presence of a strong phase noise, typical of consumer-grade equipment, will be proposed. The performance of the described algorithms will be analysed in detail through computer simulations.Index Terms-Carrier synchronization, DVB-S2 systems, iterative detection and decoding, phase noise, satellite communications.

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Section: A Gain and Snr Estimation Algorithmmentioning

confidence: 99%

On Pilot-Symbol-Assisted Carrier Synchronization for DVB-S2 Systems

Barbieri

Colavolpe

2007

IEEE Trans. on Broadcast.

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“…The SNR of clipped audio signal can be calculated with the clipped and restore signal. The SNR of clipped signals can also be estimated directly without waveform restoration using general SNR estimation methods, such as the Maximum-Likelihood Estimator of SNR [7][8][9] or Second-and Fourth-Order Moments Estimator [10][11][12]. In these methods, there are two key technological problems: 1) Clipping values, which is essential in waveform restoration, cannot be easily obtained as it is generally in a state of flux rather than a constant.…”

Section: Introductionmentioning

confidence: 99%

SNR estimation for clipped audio based on amplitude distribution

Liu

Jia

Cai

2013

2013 Ninth International Conference on Natural Computation (ICNC)

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Waveform clipping is a common problem in digital audio signal processing. Clipping effect changes the audio amplitude distribution and brings noise, which results in the decrease of audio signal-to-noise ratio (SNR). To estimate the degree of clipping effect, we propose a method for SNR estimation of clipped signals base on audio amplitude distribution. We first locate the clipping intervals of audio signal with high precision based on the differences between the amplitude probability density functions (PDFs) of normal and clipped audio signals. Then, the clipped amplitude PDF is restored using Gamma distribution to estimate the non-clipped amplitude distribution. Finally, based on the restored PDF, the estimated SNR of distorted audio signals are calculated, which can indicate the degree of clipping effect. Experiments on 3200 audios show that our approach can achieve a low error of 0.017 in clipped values locating, and an error less than 0.5dB in SNR estimation.

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“…They can be classified into two main categories [10]: maximum likelihood (ML) based estimators and method of moments. The ML estimators [11], [12] give favorable results but suffer from high computation complexity, while the moment based methods, on the other hand, have the problem to work properly when the SNR is high [10]. Moreover, these two approaches tend to require large observation length to be converged to an acceptable error, indicating that these estimators have to demand significant amount of data for a reliable estimation.…”

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confidence: 99%

On a New Approach to SNR Estimation of BPSK Signals

Moazzeni¹,

Amei²,

Ma³

et al. 2012

International Journal of Electronics and Telecommunications

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Abstract-Signal-to-noise ratio (SNR) information is required in many communication receivers and their proper operation is, to a large extent, related to the SNR estimation techniques they employ. Most of the available SNR estimators are based on approaches that either require large observation length or suffer from high computation complexity. In this paper, we propose a low complexity, yet accurate SNR estimation technique that is sufficient to yield meaningful estimation for short data records. It is shown that our estimator is fairly close to the (CRLB) for high SNR values. Numerical results also confirm that, in terms of convergence speed, the proposed technique outperforms the popular moment based method, M2M4.

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Non-data-aided signal-to-noise-ratio estimation

Cited by 85 publications

References 9 publications

On Pilot-Symbol-Assisted Carrier Synchronization for DVB-S2 Systems

On Pilot-Symbol-Assisted Carrier Synchronization for DVB-S2 Systems

SNR estimation for clipped audio based on amplitude distribution

On a New Approach to SNR Estimation of BPSK Signals

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